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May, 2023
FedDisco:基于Discrepancy-Aware协作的联邦学习
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
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Rui Ye, Mingkai Xu, Jianyu Wang, Chenxin Xu, Siheng Chen...
TL;DR
本研究考虑联邦学习领域的类别分布异质性问题,提出了一种基于数据集大小和局部与全局类别分布差异值的聚合权重调整方法(FedDisco),该方法在隐私保护、通信和计算效率上表现优异。
Abstract
This work considers the category distribution heterogeneity in
federated learning
. This issue is due to biased labeling preferences at multiple clients and is a typical setting of
data heterogeneity
. To alleviate
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