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FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering

29 Jan 2023arXiv:2301.12379archive 2025-07-28

Yongxin Guo, Xiaoying Tang, Tao Lin

Federated Learning (FL) is a machine learning paradigm that safeguards privacy by retaining client data on edge devices. However, optimizing FL in practice can be challenging due to the diverse and heterogeneous nature of the learning system. Though recent research has focused on improving the optimization of FL when distribution shifts occur among clients, ensuring global performance when multiple types of distribution shifts occur simultaneously among clients -- such as feature distribution shift, label distribution shift, and concept shift -- remain under-explored. In this paper, we identify the learning challenges posed by the simultaneous occurrence of diverse distribution shifts and propose a clustering principle to overcome these challenges. Through our research, we find that existing methods fail to address the clustering principle. Therefore, we propose a novel clustering algorithm framework, dubbed as FedRC, which adheres to our proposed clustering principle by incorporating a bi-level optimization problem and a novel objective function. Extensive experiments demonstrate that FedRC significantly outperforms other SOTA cluster-based FL methods. Our code is available at \url{https://github.com/LINs-lab/FedRC}.

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add_new_learner lins-lab/fedrc/aggregator.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 39c404c051fb3edc · report
args_to_string lins-lab/fedrc/utils/args.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 1f7cff88a51f2bb9 · report
Aggregator lins-lab/fedrc/aggregator.py official repository unverified Apache-2.0 (permissive) · ca3492d95fdd8118 · report
ClusteredAggregator lins-lab/fedrc/aggregator.py official repository unverified Apache-2.0 (permissive) · 1ca40f24804d1b46 · report
parse_args lins-lab/fedrc/utils/args.py official repository unverified Apache-2.0 (permissive) · 81dffcab091588fc · report

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ClusteringFederated Learning

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