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Sep, 2023
通过异质性引导的客户抽样加速非独立同分布联合学习
Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling
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Huancheng Chen, Haris Vikalo
TL;DR
提出了HiCS-FL(通过层次聚类抽样的联邦学习)作为一种新的客户端选择方法,其通过客户端的网络输出层更新来估计客户端数据的统计异质性,并依赖这些信息对客户端进行聚类和抽样,从而在非独立同分布的环境中实现更快的收敛和较低的训练方差。
Abstract
statistical heterogeneity
of data present at client devices in a
federated learning
(FL) system renders the training of a global model in such systems difficult. Particularly challenging are the settings where du
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