Privacy-Preserving V2X Federated Learning with Local Filtering for Sustainable Digital Last-mile Logistics

Abstract

The simultaneous pursuit of operational efficiency and strict data sovereignty presents a critical dilemma for modern urban logistics, particularly within the constrained infrastructure of developing economies. While Federated Learning (FL) offers a decentralized pathway to mitigate privacy risks, its direct application in Vehicle-to-Everything (V2X) networks is frequently compromised by heterogeneous data quality and prohibitive communication overhead. To resolve these limitations, this study proposes a Privacy-Preserving V2X Federated Learning (PPVFL) framework specifically engineered for sustainable last-mile delivery. Unlike conventional approaches that treat data quality and privacy as separate domains, the proposed method enforces a strict local outlier filtering protocol to sanitize traffic beacons at the source, integrated synergistically with sparse ternary compression and differential privacy. This unified architecture not only safeguards sensitive driver trajectories against model inversion attacks but also drastically reduces the bandwidth consumption required for global model aggregation. Empirical validation using real-world logistics data from Vietnam demonstrates that delegating quality control to the network edge enables the framework to outperform centralized and basic federated baselines in both prediction accuracy and energy efficiency. These findings articulate a scalable solution for green logistics that reconciles the trade-off between robust traffic modeling and compliance with stringent data protection standards.