  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.
