FedSA-CE: Selection-Aware Clustering for Personalized Federated Learning in Heterogeneous Vehicular Networks

Federated learning (FL) is being increasingly adopted in consumer-electronics (CE)-enabled vehicular networks to support privacy-preserving model training. However, the performance of existing FL frameworks significantly degrades under practical heterogeneous conditions, where devices differ in data distribution, computing capabilities, and wireless link quality. To address these challenges, we develop a delay-aware system model that quantifies per-CE computation and communication costs, and formulates a resource-constrained integer programming strategy for CE selection. Building on this model, we propose FedSA-CE, a selection-aware clustering framework tailored for personalized FL in dynamic vehicular environments. FedSA-CE extracts compact CE representations using truncated Singular Value Decomposition (SVD), groups CEs based on cosine similarity, and adaptively selects participants via a contribution– latency objective with time-decaying weighting. The framework prioritizes CEs with rapidly changing gradients in early training phases to accelerate convergence and gradually incorporates more diverse CEs in later stages to enhance generalization while maintaining acceptable waiting time. A similarity-weighted personalized aggregation scheme further improves local model adaptation. Experiments on 4 benchmark datasets demonstrate that FedSA-CE achieves an average accuracy improvement of 24.7% and reduces average waiting time by 83.0% compared with 7 state-of-the-art baselines. Moreover, under all extreme non-independent and identically distributed (Non-IID) settings, FedSA-CE yields an average minimum accuracy gain of 2.92% over the best-performing competing method across all datasets.