Federated learning on the go: Building stable clusters and optimizing resources on the road

Abstract

With the proliferation of Internet of Things, leveraging federated learning (FL) for collaborative model training has become paramount. It has turned into a powerful tool to analyze on-device data and produce real-time applications while safeguarding user privacy. However, in vehicular networks, the dynamic nature of vehicles, coupled with resource constraints, gives rise to new challenges for efficient FL implementation. In this paper, we address the critical problems of optimizing computational and communication resources and selecting the appropriate vehicle to participate in the process. Our proposed scheme bypasses the communication bottleneck by forming homogeneous groups based on the vehicles mobility/direction and their computing resources. Vehicle-to-Vehicle communication is then adapted within each group, and communication with an on-road edge node is orchestrated by a designated Cluster Head (CH). The latter is selected based on several factors, including connectivity index, mobility coherence, and computational resources. This selection process is designed to be robust against potential cheating attempts, which prevents nodes from avoiding the role of CH to conserve their resources. Moreover, we propose a matching algorithm that pairs each vehicular group with the appropriate edge nodes responsible for aggregating local models and facilitating communication with the server, which subsequently processes the models from all edges. The conducted experiments show promising results compared to benchmarks by achieving: (1) significantly higher amounts of trained data per iteration through strategic CH selection, leading to improved model accuracy and reduced communication overhead. Additionally, our approach demonstrates (2) efficient network load management, (3) faster convergence times in later training rounds, and (4) superior cluster stability.