Coalitional Game-Based Clustered Federated Learning for Mitigating Data Heterogeneity in Wireless Networks

Abstract

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning across mobile and edge devices. Nevertheless, the presence of non-independent and identically distributed (non-IID) data in wireless networks often leads to severe gradient divergence, slow convergence, and degraded model performance. To mitigate these challenges, this paper proposes FedCoalition, a coalition game-based clustered federated learning framework that organizes clients into collaborative coalitions according to the similarity of their model updates. The proposed method integrates a coalition formation mechanism with similarity-aware clustering to reduce gradient divergence among participating clients. In addition, each coalition designates a Coalition Head (CH) responsible for intra-cluster aggregation and model validation using auxiliary data, thereby improving model reliability and communication efficiency. The experimental results on MNIST and CIFAR-10 under non-IID data distributions demonstrate that FedCoalition significantly improves learning performance, achieving higher accuracy and faster convergence compared with existing clustered federated learning approaches.