Enhancing robustness in federated learning using minimal repair and dynamic adaptation in a scenario with client failures

Federated learning offers a promising solution for enabling collaborative model training across autonomous vehicles while preserving privacy and reducing communication overhead. However, efficiently selecting clients for the training process remains challenging, particularly in environments with statistical heterogeneity and frequent client failures. Client failures, often due to mobility or resource constraints, can significantly degrade the performance of the global model by reducing accuracy, slowing convergence, and introducing bias. This paper proposes a novel approach to enhance the robustness and reliability of FL in autonomous vehicle networks by integrating an entropy-based client selection mechanism with a minimal repair model. The entropy-based selection identifies clients with diverse and informative data, while the proposed tool substitutes failed clients with similar ones using the Hausdorff distance. Our results demonstrate that this combined approach outperforms existing methods regarding training loss, accuracy, and area under the curve, particularly in scenarios with high client dropout rates. These findings highlight the importance of considering data diversity and client substitution strategies to maintain robust FL in dynamic vehicular environments.