Hierarchical Federated Learning with Mean Field Game Device Selection for Connected Vehicle Applications

In this paper, a client-edge-cloud hierarchical federated learning (FL) model has been developed for connected vehicle applications. Generalized models are aggregated on the cloud server, while customized models trained on local data with similar data distribution are aggregated on the edge server, which mitigates the impact of data heterogeneity. To reduce the communication overhead of FL, clients will periodically update to the edge server and edge servers will periodically update to the cloud server. Moreover, we propose a mean field game-based probabilistic device selection scheme. Jointly considering their contributions and the population diversity, a fraction of devices will be selected to join the FL iteration. Taking driving range estimation as an example of connected vehicle applications in the experiment, we have shown that the proposed FL frameworks can increase the prediction accuracy by 28.9% with 5 times fewer clients’ participation, compared with the vanilla FL.