PRINCE: A Proactive Client Selection in Federated Learning for Connected and Autonomous Vehicles - Lopes, Amanda; Sousa, John; Bastos, Lucas; Pacheco, Lucas; Medeiros, Iago; Rósario, Denis; Cerqueira, Eduardo (Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2026)
Federated Learning (FL) enables cooperative training among Connected and Autonomous Vehicles (CAVs) while preserving data privacy. However, the volatility of vehicular environments, characterized by frequent link interruptions and high mobility, poses a significant obstacle to system robustness, often leading to client failures (e.g., connection, resource, aborts) that degrade global model performance. In this paper, we introduce PRINCE (Proactive Reliability-driven INtelligent Client sElection), a framework that integrates stochastic mobility modeling directly into the FL decision-making loop. In its operation, PRINCE synergizes Shannon Entropy to quantify the informational value of local data with a probabilistic mobility model to proactively filter unstable nodes before selection. Evaluation results demonstrate that PRINCE achieves a final accuracy of 83.90% and a training success rate of 61.32%. Crucially, our approach outperforms state-of-the-art reactive baselines, delivering gains of up to 9.22% in accuracy and a 3.5× improvement in resource efficiency.