UAV-empowered Vehicular Networking Scheme for Federated Learning in Delay Tolerant Environments

While vehicular federated learning (FL) systems can be used for various purposes including traffic monitoring and people flow control, since the learning process involves a large variety of network entities that exhibits different characteristics, it is inefficient to establish an end-to-end communication route for each model upload/download. In this paper, we discuss the use of delay tolerant networking (DTN) technology in transmission of FL models for unmanned aerial vehicle (UAV) empowered vehicular environments, and propose a networking scheme. The proposed scheme considers the encounter probability, the connectivity between encounter nodes, and the sociability of nodes in the packet forwarding by using a fuzzy logic approach. The importance of local model data is also considered in the buffer management of forwarder nodes, which ensures that local models with higher importance are more likely to be delivered to the central server. We use extensive simulations to evaluate the proposed scheme in terms of its effect on the federated learning, packet delivery ratio, networking overhead and communication latency by comparing with existing baselines.