AP-CFL: Clustered Federated Learning Through Dynamic Clustering and Adaptive Participation in Heterogeneous IoT

Abstract

In the advancement of collaborative intelligence within the Internet of Things (IoT), federated learning (FL) enables clients to collaboratively train a global model without centralizing raw data. However, the non-independent and identically distributed (non-IID) nature of data among clients often leads to divergent local training objectives, deteriorating the performance of the aggregated global model. To address this challenge, we propose AP-CFL, a novel clustered FL algorithm that incorporates affinity propagation to dynamically discover the clustering structure of clients without the need to predefine the number of clusters. Specifically, AP-CFL calculates the mean of absolute differences of pairwise cosine similarity to effectively cluster clients based on similarities in their data distributions. Knowledge sharing is enhanced by decoupling each cluster model into a globally shared encoder and a cluster-specific classifier, and the local training objectives are modified to improve the generalization capacity of the shared encoder. Additionally, a robust strategy is introduced to manage partial client participation by employing a time and data importance index, which mitigates the adverse effects of model staleness and maintains the integrity of the clustering structure. Extensive experiments on diverse real-world datasets demonstrate that AP-CFL outperforms existing FL baselines in non-IID settings, effectively improving model quality and convergence stability.