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.

在本文中，我们提出了一种自适应的聚类联邦学习框架，名为 FedAC，它通过解耦神经网络并为每个子模块使用不同的聚合方法，有效地将全局知识融合到簇内学习中，同时采用一种经济高效的在线模型相似度度量基于降维技术，以及一个用于改进复杂异构环境中自适应性和可扩展性的簇数微调模块。广泛的实验结果表明，相较于现有方法，FedAC 在不同的非独立同分布设置下，CIFAR-10 和 CIFAR-100 数据集的测试准确率分别提高了约 1.82% 和 12.67%。