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<!DOCTYPE html>
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<title>Energy-based Knowledge Distillation for Communication-Efficient Federated Learning</title>
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DSFL+
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<h1 class="title is-1 publication-title">Energy-based Knowledge Distillation for Communication-Efficient Federated Learning</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://azu-kitsu.com">Kitsuya Azuma</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://researchmap.jp/tomo_miyazaki">Tomo Miyazaki</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://researchmap.jp/read0180306">Shinichiro Omachi</a><sup>2</sup>,
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<span class="author-block"><sup>1</sup>School of Engineering, Tohoku University,</span>
<span class="author-block"><sup>2</sup>Graduate School of Engineering, Tohoku University</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
<b>Federated Learning</b> (FL) is a decentralized machine learning setting where many clients collaboratively train a global model without exposing their local training data. While the most used FL methods average clients' model parameters, several distillation-based methods have been proposed to aggregate model <b>outputs</b> for open unlabeled datasets and transfer the knowledge, which can significantly reduce the communication costs.
</p>
<p>
In this study, we propose a new distillation-based method called <b>DS-FL+</b>. It utilizes the <b>Energy score</b> used for out-of-distribution detection to perform thresholding, so that clients only send predictions to the server for relatively well-trained data.
</p>
<p>
Experimental results demonstrate our proposed DS-FL+ <b>can reduce communication costs by ~80%</b> for the CIFAR-10 dataset under the <b>Non-IID</b> setting.
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<h2 class="title is-3">Method</h2>
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Figure 1: The proposed DS-FL+ method with Energy-based Thresholding.
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<h2 class="title is-4">Algorithm</h2>
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Table 1: List of key notations.
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<h2 class="title is-3">Results</h2>
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Figure 2: Details of the CIFAR-10 dataset.
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Figure 3: Data partition on the private dataset.
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<p>
Figure 4: Performance comparison of three different thresholding; Energy-based (ours), MSP-based and DS-FL [2] (without thresholding).
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<p>
Figure 5: Performance comparison of four different percentage of threshold.
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<!--/ Results. -->
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{kitsuya2024energy,
author={Kitsuya, Azuma and Tomo, Miyazaki and Shinichiro, Omachi},
title={Energy-based Knowledge Distillation for Communication-Efficient Federated Learning},
journal={IEICE General Conference, ISS Junior & Student Poster Sessions},
year={2024},
}</code></pre>
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