Temporal Adaptive Clustering for Heterogeneous Clients in Federated Learning

Federated learning has emerged as a highly promising approach for training machine learning models across a decentralized network of clients, with a key focus on maintaining the privacy of data. Nevertheless, the management of system heterogeneity and the handling of time-varying interests continue to pose hurdles for conventional federated learning methodologies. This work presents temporal-based adaptive clustered federated learning as a viable solution to the difficulties mentioned above. The evaluation of clusterability is conducted by calculating the Silhouette score following each iteration of federated training. The process of model aggregation is performed at the cluster level, resulting in enhanced convergence efficiency and improved accuracy of predictions. The inclusion of temporal-based adaptiveness in clustered federated learning for time-varying environments enables the system to dynamically modify cluster configurations in response to clients joining or leaving the network. The experimental results on a real-world dataset of an electric vehicle charging station network illustrate the efficacy of the suggested approach in terms of model correctness, convergence, and adaptability. The temporal-based adaptive clustered federated learning framework has demonstrated significant advancements compared to the current state-of-the-art clustered federated learning approaches.