LAUSR

Owning to the powerful support of 5G/B5G technologies, it is promising that, in a vehicular network, the ever-increasing demands for artificial intelligence applications along with privacy concerns can be fulfilled by introducing federated learning (FL) into vehicular networks. However, in such a 5G/B5G cellular-based vehicular network, distributed clients, i.e., vehicles, usually suffer from intermittent connectivity to the uplink base station (BS) due to the small cell networking and their mobility, and thereby the FL execution time and the learning performance cannot be guaranteed. In order to ensure a required execution time and the FL performance, we propose to investigate the client selection schemes in the uplink-downlink decoupled 5G/B5G networks. Due to the bursty feature of uplink transmissions, we first incorporate a flexible delay analytical tool, i.e., martingale theory, to characterize the access delay of each vehicle, and represent it in the form of delay violation probability. Then, given an energy budget, adjustable parameters are optimized to maximize the global learning performance. Supposing that all the clients participate in the FL and they can outperform the systematic FL performance requirement, the client selection methods under different criteria are further investigated. Especially, we propose a low-complexity algorithm to select clients in terms of enhancing energy efficiency. Numerical results are provided to demonstrate the effectiveness of the proposed client selection method in both guaranteeing the FL execution time and meeting the systematic learning performance. The energy efficiency of the vehicular FL system can also be enhanced by employing the proposed method.