FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
<|reference_start|>FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data: 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, which (1) efficiently integrates global knowledge into intra-cluster learning by decoupling neural networks and utilizing distinct aggregation methods for each submodule, significantly enhancing performance; (2) includes a costeffective online model similarity metric based on dimensionality reduction; (3) incorporates a cluster number fine-tuning module for improved adaptability and scalability in complex, heterogeneous environments. Extensive experiments show that FedAC achieves superior empirical performance, increasing the test accuracy by around 1.82% and 12.67% on CIFAR-10 and CIFAR-100 datasets, respectively, under different non-IID settings compared to SOTA methods.<|reference_end|>
@article{zhang2024fedac:,
    title={FedAC: An Adaptive Clustered Federated Learning Framework for
  Heterogeneous Data},
    author={Yuxin Zhang, Haoyu Chen, Zheng Lin, Zhe Chen, Jin Zhao},
    journal={arXiv preprint arXiv:2403.16460},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16460},
    primaryClass={cs.LG cs.AI cs.DC}
}