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FedAC: A Adaptive Clustered Federated Learning Framework for Heterogeneous Data
March 26, 2024, 4:42 a.m. | Yuxin Zhang, Haoyu Chen, Zheng Lin, Zhe Chen, Jin Zhao
cs.LG updates on arXiv.org arxiv.org
Abstract: Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for cluster-wise model training. However, current CFL methods struggle due to inadequate integration of global and intra-cluster knowledge and the absence of an efficient online model similarity metric, while treating the cluster count as a fixed hyperparameter limits flexibility and robustness. In this paper, we propose an adaptive CFL framework, named FedAC, …
abstract arxiv cluster cs.ai cs.dc cs.lg current data federated learning framework global however integration knowledge performance stemming struggle training type wise
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