Poster: Delay and Energy-Efficient Client Selection for Federated Learning in Vehicular Networks

This paper proposes a delay and energy-aware clustering-based client selection scheme for federated learning in vehicular networks. We propose an algorithm that selects the appropriate number of vehicles for local training, minimizing delay and energy consumption while ensuring model performance. The simulation results demonstrate that the proposed algorithm achieves lower delay and energy consumption compared to benchmark methods, for both IID (independent and identically distributed) and non-IID datasets.