FedDPC: A Density Peak Clustering-based Federated Learning Algorithm

Federated learning (FL) is widely applied in edge computing scenarios to protect data privacy, but the assumption of independent and identically distributed (IID) data across clients often does not hold in practice, leading to performance degradation. Among the many methods proposed to address data heterogeneity, clustered federated learning (CFL) has emerged as a popular approach. Current CFL methods commonly adopt the expectation-maximization (EM) algorithm, which mitigates the problem of data heterogeneity. However, they still face challenges such as requiring more clients than traditional federated learning and introducing randomness in the clustering process. To overcome these limitations, we propose FedDPC, which integrates the density peak clustering (DPC) algorithm. This approach provides the advantages of not requiring additional clients and having a non-random partitioning strategy. Finally, we conducted experiments to demonstrate the feasibility and effectiveness of our proposed method.