Intelligent Federated Learning Framework for Non-colocated and Heterogeneous Datasets

Federated learning has significant potential for distributed model training while preserving privacy, but it faces challenges related to convergence, fairness, and interpretability due to the heterogeneity of non-colocated datasets. This study proposes an Intelligent Federated Learning Framework (IFLF) to address these challenges through an adaptive approach using a multi-layer architecture consisting of Data, Client, Aggregation, Adaptation, and Optimization, and Interpretability layers. The framework demonstrates stable convergence under non-IID data distributions, with aggregation strategies supporting balanced optimization. The learning-rate modulation approach contributes to stable training by integrating heterogeneous client updates and reducing divergence during optimization. Explainable AI techniques, including SHAP and LIME, are incorporated to improve transparency at both client and global levels. The IFLF is evaluated on four benchmark datasets (FEMNIST (vision), FLamby (healthcare imaging), FedGraphNN (graph learning), and CICIDS2017 (cybersecurity)). The framework achieved an average accuracy of 92.8%, with faster convergence and reduced performance variability across clients.