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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.20988">arXiv:2405.20988</a> <span> [<a href="https://arxiv.org/pdf/2405.20988">pdf</a>, <a href="https://arxiv.org/format/2405.20988">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Theologitis%2C+M">Michail Theologitis</a>, <a href="/search/cs?searchtype=author&query=Frangias%2C+G">Georgios Frangias</a>, <a href="/search/cs?searchtype=author&query=Anestis%2C+G">Georgios Anestis</a>, <a href="/search/cs?searchtype=author&query=Samoladas%2C+V">Vasilis Samoladas</a>, <a href="/search/cs?searchtype=author&query=Deligiannakis%2C+A">Antonios Deligiannakis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.20988v4-abstract-short" style="display: inline;"> The ever-growing volume and decentralized nature of data, coupled with the need to harness it and extract knowledge, have led to the extensive use of distributed deep learning (DDL) techniques for training. These techniques rely on local training performed at distributed nodes using locally collected data, followed by a periodic synchronization process that combines these models to create a unifie… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20988v4-abstract-full').style.display = 'inline'; document.getElementById('2405.20988v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20988v4-abstract-full" style="display: none;"> The ever-growing volume and decentralized nature of data, coupled with the need to harness it and extract knowledge, have led to the extensive use of distributed deep learning (DDL) techniques for training. These techniques rely on local training performed at distributed nodes using locally collected data, followed by a periodic synchronization process that combines these models to create a unified global model. However, the frequent synchronization of deep learning models, encompassing millions to many billions of parameters, creates a communication bottleneck, severely hindering scalability. Worse yet, DDL algorithms typically waste valuable bandwidth and render themselves less practical in bandwidth-constrained federated settings by relying on overly simplistic, periodic, and rigid synchronization schedules. These inefficiencies make the training process increasingly impractical as they demand excessive time for data communication. To address these shortcomings, we propose Federated Dynamic Averaging (FDA), a communication-efficient DDL strategy that dynamically triggers synchronization based on the value of the model variance. In essence, the costly synchronization step is triggered only if the local models -- initialized from a common global model after each synchronization -- have significantly diverged. This decision is facilitated by the transmission of a small local state from each distributed node. Through extensive experiments across a wide range of learning tasks we demonstrate that FDA reduces communication cost by orders of magnitude, compared to both traditional and cutting-edge communication-efficient algorithms. Additionally, we show that FDA maintains robust performance across diverse data heterogeneity settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20988v4-abstract-full').style.display = 'none'; document.getElementById('2405.20988v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted as research paper at EDBT 2025</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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