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22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 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flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 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1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline 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data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> 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0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 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6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="SYPMe6TnojuT79avr5sa4kGardTAjoJrWJZmy+vuPSkzOPlj6fwOrOvNIx5gIP+EF36ZaBoFcQepwS7QxlWzvA==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" 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id=\"user-content-flea\" class=\"anchor\" aria-label=\"Permalink: FLea\" href=\"#flea\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repository includes the code to reproduce our experiments in the submission entitled\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eFLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-Preserving Feature Augmentation\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eaccepted by KDD 2024 Research Track\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install requirements:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"conda env create -f environment.yaml\nconda activate FLea\nconda env list\"\u003e\u003cpre lang=\"setup\" class=\"notranslate\"\u003e\u003ccode\u003econda env create -f environment.yaml\nconda activate FLea\nconda env list\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe mainly use \u003cem\u003etorch\u003c/em\u003e, \u003cem\u003etorchvision\u003c/em\u003e, \u003cem\u003etorchaudio\u003c/em\u003e, \u003cem\u003enumpy\u003c/em\u003e, \u003cem\u003esklearn\u003c/em\u003e,\u003cem\u003epandas\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCode structure\u003c/h2\u003e\u003ca id=\"user-content-code--structure\" class=\"anchor\" aria-label=\"Permalink: Code structure\" href=\"#code--structure\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"├── FLea\n│ ├── data # data path\n│ │ ├── CIFAR10\n│ │ ├── UrbanSound8K\n│ │ ├── UCI_HAR\n│ ├── Utils # function path\n│ │ ├── data_utils # data partition \n│ │ ├── model_utils\n│ │ ├── argparse\n│ │ ├── functional\n│ ├── FLAlgorithms # main algorithm path\n│ │ ├── servers # code for server function\n│ │ ├── users # code for client function\n│ │ ├── trainmodel # model architecture\n│ ├── run # script for running code\n│ │ ├── Image\n│ │ ├── Audio\n│ │ ├── HAR\n│ ├── models # saved models and logs\n│ │ ├── saved\n│ │ ├── logs\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e├── FLea\n│ ├── data # data path\n│ │ ├── CIFAR10\n│ │ ├── UrbanSound8K\n│ │ ├── UCI_HAR\n│ ├── Utils # function path\n│ │ ├── data_utils # data partition \n│ │ ├── model_utils\n│ │ ├── argparse\n│ │ ├── functional\n│ ├── FLAlgorithms # main algorithm path\n│ │ ├── servers # code for server function\n│ │ ├── users # code for client function\n│ │ ├── trainmodel # model architecture\n│ ├── run # script for running code\n│ │ ├── Image\n│ │ ├── Audio\n│ │ ├── HAR\n│ ├── models # saved models and logs\n│ │ ├── saved\n│ │ ├── logs\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDataset and split\u003c/h2\u003e\u003ca id=\"user-content-dataset-and-split\" class=\"anchor\" aria-label=\"Permalink: Dataset and split\" href=\"#dataset-and-split\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe use three public datasets, put in \u003ccode\u003edata\u003c/code\u003e path.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCIFAR10 will be downloaded automatically for the first run.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eUrbanSound8K can be downloaded from \u003ca href=\"https://urbansounddataset.weebly.com/urbansound8k.html\" rel=\"nofollow\"\u003eUrbanSound8K - Urban Sound Datasets (weebly.com)\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eUCI_HAR can be downloaded from \u003ca href=\"https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones\" rel=\"nofollow\"\u003eHuman Activity Recognition Using Smartphones - UCI Machine Learning Repository\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOfficial training and testing split are used in our experiments.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWe split the training set data into K clients.\u003c/strong\u003e To simulate label skew, we consider quantity-based skew (Qua(q) with q being the number of presented classes) and distribution-based skew (Dir(u) with u controlling the class skewness). The partition method is adapted from \u003ca href=\"/XTxiatong/FLea/blob/master\"\u003eFedLab-NLP/fedlab/utils/dataset/functional.py\u003c/a\u003e. To mimic different levels of data scarcity, we distribute the training data to K clients, where K is determined by the average local data size, which is set to as small as 100 and 50 for experiments.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAn example of the CIFAR-10 data distribution is visualized below. The training data for 10 classes are distributed across 100 clients, showcasing a quantity-based skew, with each client having only 3 classes of data. Each column represents one client, and the number of samples is denoted by color.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/XTxiatong/FLea/blob/master/cifar10_q3.png\"\u003e\u003cimg src=\"/XTxiatong/FLea/raw/master/cifar10_q3.png\" alt=\"cifar10_q3\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining FLea\u003c/h2\u003e\u003ca id=\"user-content-training-flea\" class=\"anchor\" aria-label=\"Permalink: Training FLea\" href=\"#training-flea\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThere are a few setups in our code:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"--dataset: The dataset to use: 'Cifar10', 'UrbanSound', 'UCI_HAR' \\\n--split_method: The data particiption method: 'quantity','distribution' \\\n--split_para: Data particiption paraemter: 3, 0.5, 0.1 \\\n--split_num 1000: The number of clients: K \\\n--client_num 100: The number of clients per round \\\n--class_num: The number of classes \\\n--local_epochs: The number of epoches per round\\\n--batch_size: Batch size for local training: 32, 64 \\\n--num_global_iters: The total number of rounds: 100 \\\n--personal_learning_rate: Local learning rate： 0.001 \\\n--modelname: Model: 'MOBNET', 'AudioNet','HARNet' \\\n--algorithm: Our proposed FL method and baselines: 'FLea', 'FedAvg', 'FedProx', 'FedDecor','FedLC', 'FedNTD', 'FedBR', 'CCVR', 'FedMix_pre', 'FedData_pre'\\\n--loss: Local training lossfuction: 'MCE_DeC_KL', 'CE', 'MCE' \\\n--layer: From which CNN block the activations are eacted for FLea: 1 \\\n--fea_percent: The precentage of the data/featuers shared out: 0.1 \\\n--seed: random seed: 0, 1, 10, 100, 100, 1000 \\\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e--dataset: The dataset to use: 'Cifar10', 'UrbanSound', 'UCI_HAR' \\\n--split_method: The data particiption method: 'quantity','distribution' \\\n--split_para: Data particiption paraemter: 3, 0.5, 0.1 \\\n--split_num 1000: The number of clients: K \\\n--client_num 100: The number of clients per round \\\n--class_num: The number of classes \\\n--local_epochs: The number of epoches per round\\\n--batch_size: Batch size for local training: 32, 64 \\\n--num_global_iters: The total number of rounds: 100 \\\n--personal_learning_rate: Local learning rate： 0.001 \\\n--modelname: Model: 'MOBNET', 'AudioNet','HARNet' \\\n--algorithm: Our proposed FL method and baselines: 'FLea', 'FedAvg', 'FedProx', 'FedDecor','FedLC', 'FedNTD', 'FedBR', 'CCVR', 'FedMix_pre', 'FedData_pre'\\\n--loss: Local training lossfuction: 'MCE_DeC_KL', 'CE', 'MCE' \\\n--layer: From which CNN block the activations are eacted for FLea: 1 \\\n--fea_percent: The precentage of the data/featuers shared out: 0.1 \\\n--seed: random seed: 0, 1, 10, 100, 100, 1000 \\\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAll the running scripts are provided in folder \u003ccode\u003e./run/\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo run the code for FLea, using CIFAR10 data, Dir(0.1), K=1000 (average local data size 50)\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" sh run/Image/run_FLea.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e sh run/Image/run_FLea.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo run the code for FLea, using audio data, Qua(3), K= 70 (average local data size 100)\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" sh run/Audio/run_FLea_audio.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e sh run/Audio/run_FLea_audio.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo run the code for FLea, using HAR data, Dir(0.1), K=150 (average local data size 50)\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" sh run/HAR/FLea_HAR.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e sh run/HAR/FLea_HAR.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBaselines\u003c/h2\u003e\u003ca id=\"user-content-baselines\" class=\"anchor\" aria-label=\"Permalink: Baselines\" href=\"#baselines\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBaseline implementations are included in our code base. We provide the running script for \u003ccode\u003eFedAvg, FedProx, FedDecor,FedLC, FedNTD, FedBR, CCVR, FedMix, FedData\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo run the code for \u003ccode\u003eFedAvg\u003c/code\u003e, using CIFAR10 data, distribution skew, K=1000 (average local data size 50)\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" sh run/Image/run_FedAvg.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e sh run/Image/run_FedAvg.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo run the code for FedMix,\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" sh run/Image/run_FedMix.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e sh run/Image/run_FedMix.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOur training logs can also be found from \u003ccode\u003e./models/logs/\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor example, the model trained for HAR data, using K=150, partition Dir(0.1) can be found from \u003ccode\u003eFLea_HAR_K150_Dir0.1.log\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"================================================================================\nSummary of training process:\nDataset : UCI_HAR\nBatch size : 32\nLearing rate : 0.001\nNumber of total clients: 150\nSplit method : distribution\nSplit parameter : 0.1\nClients per round : 15\nNumber of global rounds: 100\nNumber of local rounds : 5\nFeature from layer : 1\nFeature percentage : 0.1\nIs interplolated : False\nLocal training loss : MCE_DeC_KL\nLoss of beta : 1.0\nAlgorithm : FLea\nModelname : HARNet\nMode : training\nSeed : 0\n================================================================================\n\nDataset: UCI_HAR\ntraining: 7352 testing: 2947\n\n.....\n\nNumber of users per round / total users: 15 / 150\nFinished creating FL server.\n=== Training starts: algorithm FLea ===\n-------------Round number: 0 -------------\n......\n-------------Round number: 57 -------------\n\nGlobal Model Acc on global data: 0.6599932134373939 \nsave a model\n......\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e================================================================================\nSummary of training process:\nDataset : UCI_HAR\nBatch size : 32\nLearing rate : 0.001\nNumber of total clients: 150\nSplit method : distribution\nSplit parameter : 0.1\nClients per round : 15\nNumber of global rounds: 100\nNumber of local rounds : 5\nFeature from layer : 1\nFeature percentage : 0.1\nIs interplolated : False\nLocal training loss : MCE_DeC_KL\nLoss of beta : 1.0\nAlgorithm : FLea\nModelname : HARNet\nMode : training\nSeed : 0\n================================================================================\n\nDataset: UCI_HAR\ntraining: 7352 testing: 2947\n\n.....\n\nNumber of users per round / total users: 15 / 150\nFinished creating FL server.\n=== Training starts: algorithm FLea ===\n-------------Round number: 0 -------------\n......\n-------------Round number: 57 -------------\n\nGlobal Model Acc on global data: 0.6599932134373939 \nsave a model\n......\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTrained model checkpoints are saved in \u003ccode\u003e./models/saved/\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor example, the model trained for HAR data, using K=150, partition Dir(0.1) can be found from \u003ccode\u003e./models/saved/server_FLea_HARNet_UCI_HAR_loss_MCE_DeC_KL_epoch_5_100_client_150_split_distribution_0.1.pt\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePrivacy analysis\u003c/h2\u003e\u003ca id=\"user-content-privacy-analysis\" class=\"anchor\" aria-label=\"Permalink: Privacy analysis\" href=\"#privacy-analysis\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTraining and testing the data reconstruction attack: \u003ccode\u003epython test_reconstraction.py\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/XTxiatong/FLea/blob/master/recons_example.png\"\u003e\u003cimg src=\"/XTxiatong/FLea/raw/master/recons_example.png\" alt=\"recons_example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"FLea","anchor":"flea","htmlText":"FLea"},{"level":2,"text":"Requirements","anchor":"requirements","htmlText":"Requirements"},{"level":2,"text":"Code structure","anchor":"code--structure","htmlText":"Code structure"},{"level":2,"text":"Dataset and split","anchor":"dataset-and-split","htmlText":"Dataset and split"},{"level":2,"text":"Training FLea","anchor":"training-flea","htmlText":"Training FLea"},{"level":2,"text":"Baselines","anchor":"baselines","htmlText":"Baselines"},{"level":2,"text":"Results","anchor":"results","htmlText":"Results"},{"level":2,"text":"Privacy analysis","anchor":"privacy-analysis","htmlText":"Privacy analysis"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FXTxiatong%2FFLea"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div data-target="react-partial.reactRoot"><style data-styled="true" data-styled-version="5.3.11">.iVEunk{margin-top:16px;margin-bottom:16px;}/*!sc*/ 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class="Box-sc-g0xbh4-0 izFOf"><button data-component="IconButton" type="button" aria-label="Open more actions menu" aria-haspopup="true" aria-expanded="false" tabindex="0" class="prc-Button-ButtonBase-c50BI prc-Button-IconButton-szpyj" data-loading="false" data-no-visuals="true" data-size="medium" data-variant="default" aria-describedby=":R75ab:-loading-announcement" id=":R75ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-kebab-horizontal" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M8 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3ZM1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Zm13 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path></svg></button></div></div></div><div class="Box-sc-g0xbh4-0 vIPPs"><div data-hpc="true"><button hidden="" data-testid="focus-next-element-button" data-hotkey="j"></button><button hidden="" data-testid="focus-previous-element-button" data-hotkey="k"></button><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading" id="folders-and-files">Folders and files</h2><table aria-labelledby="folders-and-files" class="Box-sc-g0xbh4-0 fdROMU"><thead class="Box-sc-g0xbh4-0 jGKpsv"><tr class="Box-sc-g0xbh4-0 jdgHnn"><th colSpan="2" class="Box-sc-g0xbh4-0 bQivRW"><span class="text-bold">Name</span></th><th colSpan="1" class="Box-sc-g0xbh4-0 ldkMIO"><span class="text-bold">Name</span></th><th class="hide-sm"><div title="Last commit message" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit message</span></div></th><th colSpan="1" class="Box-sc-g0xbh4-0 jMbWeI"><div title="Last commit date" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit date</span></div></th></tr></thead><tbody><tr class="Box-sc-g0xbh4-0 gpqjiB"><td colSpan="3" class="bgColor-muted p-1 rounded-top-2"><div class="Box-sc-g0xbh4-0 dzCJzi"><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">Latest commit</h2><div style="width:120px" class="Skeleton Skeleton--text" data-testid="loading"> </div><div class="d-flex flex-shrink-0 gap-2"><div data-testid="latest-commit-details" class="d-none d-sm-flex flex-items-center"></div><div class="d-flex gap-2"><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">History</h2><a href="/XTxiatong/FLea/commits/master/" class="prc-Button-ButtonBase-c50BI d-none d-lg-flex LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":Raqj8pab:-loading-announcement"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x"><span class="fgColor-default">8 Commits</span></span></span></a><div class="d-sm-none"></div><div class="d-flex d-lg-none"><span role="tooltip" aria-label="8 Commits" id="history-icon-button-tooltip" class="Tooltip__TooltipBase-sc-17tf59c-0 hWlpPn tooltipped-n"><a href="/XTxiatong/FLea/commits/master/" class="prc-Button-ButtonBase-c50BI LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":R1iqj8pab:-loading-announcement history-icon-button-tooltip"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span></span></a></span></div></div></div></div></td></tr><tr class="react-directory-row undefined" id="folder-row-0"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="FLAlgorithms" aria-label="FLAlgorithms, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/FLAlgorithms">FLAlgorithms</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="FLAlgorithms" aria-label="FLAlgorithms, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/FLAlgorithms">FLAlgorithms</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-1"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="data" aria-label="data, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/data">data</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="data" aria-label="data, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/data">data</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-2"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="models" aria-label="models, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/models">models</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="models" aria-label="models, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/models">models</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-3"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="run" aria-label="run, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/run">run</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="run" aria-label="run, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/run">run</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-4"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="utils" aria-label="utils, (Directory)" class="Link--primary" href="/XTxiatong/FLea/tree/master/utils">utils</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 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1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="cifar10_q3.png" aria-label="cifar10_q3.png, (File)" class="Link--primary" href="/XTxiatong/FLea/blob/master/cifar10_q3.png">cifar10_q3.png</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-6"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file 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focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">FLea</h1><a id="user-content-flea" class="anchor" aria-label="Permalink: FLea" href="#flea"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repository includes the code to reproduce our experiments in the submission entitled</p> <p dir="auto"><em>FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-Preserving Feature Augmentation</em></p> <p dir="auto">accepted by KDD 2024 Research Track</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Requirements</h2><a id="user-content-requirements" class="anchor" aria-label="Permalink: Requirements" href="#requirements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To install requirements:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="conda env create -f environment.yaml conda activate FLea conda env list"><pre lang="setup" class="notranslate"><code>conda env create -f environment.yaml conda activate FLea conda env list </code></pre></div> <p dir="auto">We mainly use <em>torch</em>, <em>torchvision</em>, <em>torchaudio</em>, <em>numpy</em>, <em>sklearn</em>,<em>pandas</em></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Code structure</h2><a id="user-content-code--structure" class="anchor" aria-label="Permalink: Code structure" href="#code--structure"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="├── FLea │ ├── data # data path │ │ ├── CIFAR10 │ │ ├── UrbanSound8K │ │ ├── UCI_HAR │ ├── Utils # function path │ │ ├── data_utils # data partition │ │ ├── model_utils │ │ ├── argparse │ │ ├── functional │ ├── FLAlgorithms # main algorithm path │ │ ├── servers # code for server function │ │ ├── users # code for client function │ │ ├── trainmodel # model architecture │ ├── run # script for running code │ │ ├── Image │ │ ├── Audio │ │ ├── HAR │ ├── models # saved models and logs │ │ ├── saved │ │ ├── logs"><pre class="notranslate"><code>├── FLea │ ├── data # data path │ │ ├── CIFAR10 │ │ ├── UrbanSound8K │ │ ├── UCI_HAR │ ├── Utils # function path │ │ ├── data_utils # data partition │ │ ├── model_utils │ │ ├── argparse │ │ ├── functional │ ├── FLAlgorithms # main algorithm path │ │ ├── servers # code for server function │ │ ├── users # code for client function │ │ ├── trainmodel # model architecture │ ├── run # script for running code │ │ ├── Image │ │ ├── Audio │ │ ├── HAR │ ├── models # saved models and logs │ │ ├── saved │ │ ├── logs </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Dataset and split</h2><a id="user-content-dataset-and-split" class="anchor" aria-label="Permalink: Dataset and split" href="#dataset-and-split"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We use three public datasets, put in <code>data</code> path.</p> <ul dir="auto"> <li> <p dir="auto">CIFAR10 will be downloaded automatically for the first run.</p> </li> <li> <p dir="auto">UrbanSound8K can be downloaded from <a href="https://urbansounddataset.weebly.com/urbansound8k.html" rel="nofollow">UrbanSound8K - Urban Sound Datasets (weebly.com)</a></p> </li> <li> <p dir="auto">UCI_HAR can be downloaded from <a href="https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones" rel="nofollow">Human Activity Recognition Using Smartphones - UCI Machine Learning Repository</a></p> <p dir="auto">Official training and testing split are used in our experiments.</p> </li> </ul> <p dir="auto"><strong>We split the training set data into K clients.</strong> To simulate label skew, we consider quantity-based skew (Qua(q) with q being the number of presented classes) and distribution-based skew (Dir(u) with u controlling the class skewness). The partition method is adapted from <a href="/XTxiatong/FLea/blob/master">FedLab-NLP/fedlab/utils/dataset/functional.py</a>. To mimic different levels of data scarcity, we distribute the training data to K clients, where K is determined by the average local data size, which is set to as small as 100 and 50 for experiments.</p> <p dir="auto">An example of the CIFAR-10 data distribution is visualized below. The training data for 10 classes are distributed across 100 clients, showcasing a quantity-based skew, with each client having only 3 classes of data. Each column represents one client, and the number of samples is denoted by color.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/XTxiatong/FLea/blob/master/cifar10_q3.png"><img src="/XTxiatong/FLea/raw/master/cifar10_q3.png" alt="cifar10_q3" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Training FLea</h2><a id="user-content-training-flea" class="anchor" aria-label="Permalink: Training FLea" href="#training-flea"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">There are a few setups in our code:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="--dataset: The dataset to use: 'Cifar10', 'UrbanSound', 'UCI_HAR' \ --split_method: The data particiption method: 'quantity','distribution' \ --split_para: Data particiption paraemter: 3, 0.5, 0.1 \ --split_num 1000: The number of clients: K \ --client_num 100: The number of clients per round \ --class_num: The number of classes \ --local_epochs: The number of epoches per round\ --batch_size: Batch size for local training: 32, 64 \ --num_global_iters: The total number of rounds: 100 \ --personal_learning_rate: Local learning rate： 0.001 \ --modelname: Model: 'MOBNET', 'AudioNet','HARNet' \ --algorithm: Our proposed FL method and baselines: 'FLea', 'FedAvg', 'FedProx', 'FedDecor','FedLC', 'FedNTD', 'FedBR', 'CCVR', 'FedMix_pre', 'FedData_pre'\ --loss: Local training lossfuction: 'MCE_DeC_KL', 'CE', 'MCE' \ --layer: From which CNN block the activations are eacted for FLea: 1 \ --fea_percent: The precentage of the data/featuers shared out: 0.1 \ --seed: random seed: 0, 1, 10, 100, 100, 1000 \"><pre class="notranslate"><code>--dataset: The dataset to use: 'Cifar10', 'UrbanSound', 'UCI_HAR' \ --split_method: The data particiption method: 'quantity','distribution' \ --split_para: Data particiption paraemter: 3, 0.5, 0.1 \ --split_num 1000: The number of clients: K \ --client_num 100: The number of clients per round \ --class_num: The number of classes \ --local_epochs: The number of epoches per round\ --batch_size: Batch size for local training: 32, 64 \ --num_global_iters: The total number of rounds: 100 \ --personal_learning_rate: Local learning rate： 0.001 \ --modelname: Model: 'MOBNET', 'AudioNet','HARNet' \ --algorithm: Our proposed FL method and baselines: 'FLea', 'FedAvg', 'FedProx', 'FedDecor','FedLC', 'FedNTD', 'FedBR', 'CCVR', 'FedMix_pre', 'FedData_pre'\ --loss: Local training lossfuction: 'MCE_DeC_KL', 'CE', 'MCE' \ --layer: From which CNN block the activations are eacted for FLea: 1 \ --fea_percent: The precentage of the data/featuers shared out: 0.1 \ --seed: random seed: 0, 1, 10, 100, 100, 1000 \ </code></pre></div> <p dir="auto">All the running scripts are provided in folder <code>./run/</code></p> <p dir="auto">To run the code for FLea, using CIFAR10 data, Dir(0.1), K=1000 (average local data size 50)</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" sh run/Image/run_FLea.sh"><pre class="notranslate"><code> sh run/Image/run_FLea.sh </code></pre></div> <p dir="auto">To run the code for FLea, using audio data, Qua(3), K= 70 (average local data size 100)</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" sh run/Audio/run_FLea_audio.sh"><pre class="notranslate"><code> sh run/Audio/run_FLea_audio.sh </code></pre></div> <p dir="auto">To run the code for FLea, using HAR data, Dir(0.1), K=150 (average local data size 50)</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" sh run/HAR/FLea_HAR.sh"><pre class="notranslate"><code> sh run/HAR/FLea_HAR.sh </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Baselines</h2><a id="user-content-baselines" class="anchor" aria-label="Permalink: Baselines" href="#baselines"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Baseline implementations are included in our code base. We provide the running script for <code>FedAvg, FedProx, FedDecor,FedLC, FedNTD, FedBR, CCVR, FedMix, FedData</code></p> <p dir="auto">To run the code for <code>FedAvg</code>, using CIFAR10 data, distribution skew, K=1000 (average local data size 50)</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" sh run/Image/run_FedAvg.sh"><pre class="notranslate"><code> sh run/Image/run_FedAvg.sh </code></pre></div> <p dir="auto">To run the code for FedMix,</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" sh run/Image/run_FedMix.sh"><pre class="notranslate"><code> sh run/Image/run_FedMix.sh </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Results</h2><a id="user-content-results" class="anchor" aria-label="Permalink: Results" href="#results"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Our training logs can also be found from <code>./models/logs/</code></p> <p dir="auto">For example, the model trained for HAR data, using K=150, partition Dir(0.1) can be found from <code>FLea_HAR_K150_Dir0.1.log</code></p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="================================================================================ Summary of training process: Dataset : UCI_HAR Batch size : 32 Learing rate : 0.001 Number of total clients: 150 Split method : distribution Split parameter : 0.1 Clients per round : 15 Number of global rounds: 100 Number of local rounds : 5 Feature from layer : 1 Feature percentage : 0.1 Is interplolated : False Local training loss : MCE_DeC_KL Loss of beta : 1.0 Algorithm : FLea Modelname : HARNet Mode : training Seed : 0 ================================================================================ Dataset: UCI_HAR training: 7352 testing: 2947 ..... Number of users per round / total users: 15 / 150 Finished creating FL server. === Training starts: algorithm FLea === -------------Round number: 0 ------------- ...... -------------Round number: 57 ------------- Global Model Acc on global data: 0.6599932134373939 save a model ......"><pre class="notranslate"><code>================================================================================ Summary of training process: Dataset : UCI_HAR Batch size : 32 Learing rate : 0.001 Number of total clients: 150 Split method : distribution Split parameter : 0.1 Clients per round : 15 Number of global rounds: 100 Number of local rounds : 5 Feature from layer : 1 Feature percentage : 0.1 Is interplolated : False Local training loss : MCE_DeC_KL Loss of beta : 1.0 Algorithm : FLea Modelname : HARNet Mode : training Seed : 0 ================================================================================ Dataset: UCI_HAR training: 7352 testing: 2947 ..... Number of users per round / total users: 15 / 150 Finished creating FL server. === Training starts: algorithm FLea === -------------Round number: 0 ------------- ...... -------------Round number: 57 ------------- Global Model Acc on global data: 0.6599932134373939 save a model ...... </code></pre></div> <p dir="auto">Trained model checkpoints are saved in <code>./models/saved/</code></p> <p dir="auto">For example, the model trained for HAR data, using K=150, partition Dir(0.1) can be found from <code>./models/saved/server_FLea_HARNet_UCI_HAR_loss_MCE_DeC_KL_epoch_5_100_client_150_split_distribution_0.1.pt</code></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Privacy analysis</h2><a id="user-content-privacy-analysis" class="anchor" aria-label="Permalink: Privacy analysis" href="#privacy-analysis"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Training and testing the data reconstruction attack: <code>python test_reconstraction.py</code></p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/XTxiatong/FLea/blob/master/recons_example.png"><img src="/XTxiatong/FLea/raw/master/recons_example.png" alt="recons_example" style="max-width: 100%;"></a></p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" 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