Residence Time Aware Client Selection in Federated Learning in Vehicular Network

Federated learning is experiencing great growth thanks to its many advantages, particularly in terms of security and cost reduction. With the progress of 5G/6G networks, this approach is increasingly explored for connected vehicle networks in which vehicles collaboratively build learning models under the orchestration of network base stations. However, existing works neglect the very volatile context of both radio network and vehicle mobility in the design of federated learning solution. Indeed, vehicles are not fixed entities and their residence time, i.e. connection duration to a base station, is a function of their mobility and the load of the base station. In this paper we present a federated learning approach adapted for vehicular networks. This approach is based on two models that we define: a model for predicting vehicle residence times in base stations based on their speeds and positions; a model for selecting vehicles that participate in federated learning which takes into account predicted residence times. Test results based on real data and simulation data show that our approach provides better learning performance in terms of convergence speed and precision.