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 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%2C current CFL methods struggle due to inadequate integration of global and intra-cluster knowledge and the absence of an efficient online model similarity metric%2C while treating the cluster count as a fixed hyperparameter limits flexibility and robustness. In this paper%2C we propose an adaptive CFL framework%2C named FedAC%2C which (1) efficiently integrates global knowledge into intra-cluster learning by decoupling neural networks and utilizing distinct aggregation methods for each submodule%2C significantly enhancing performance%3B (2) includes a costeffective online model similarity metric based on dimensionality reduction%3B (3) incorporates a cluster number fine-tuning module for improved adaptability and scalability in complex%2C heterogeneous environments. Extensive experiments show that FedAC achieves superior empirical performance%2C increasing the test accuracy by around 1.82%25 and 12.67%25 on CIFAR-10 and CIFAR-100 datasets%2C respectively%2C under different non-IID settings compared to SOTA methods.?quality=80&w=800)
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