AMGCN-FL: Adaptive Multi-Graph Convolutional Networks for Personalized Federated Learning in Industrial IoT Environments

Industrial Internet of Things IIoT environments generate vast amounts of heterogeneous data across distributed devices, presenting unique challenges for machine learning applications. Federated Learning FL has emerged as a promising paradigm for collaborative model training while preserving data privacy. However, existing FL approaches struggle with the non-IID Independent and Identically Distributed nature of industrial data, leading to suboptimal personalization. In this paper, we propose AMGCN-FL, an Adaptive Multi-Graph Convolutional Network for Federated Learning that addresses the challenges of personalized learning in heterogeneous IIoT environments. Our approach leverages adaptive graph structures to capture complex relationships between clients and introduces a novel parameter-efficient knowledge transfer mechanism. Theoretical analysis demonstrates the convergence properties of our algorithm under non-IID data distributions. Extensive experiments on benchmark datasets show that AMGCN-FL consistently outperforms state-of-the-art personalized FL methods, achieving up to 5.8 improvement in accuracy while maintaining communication efficiency. The proposed method demonstrates robust performance across various degrees of data heterogeneity, making it particularly suitable for real-world industrial applications.