A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing

Fateme Mazloomi (), Shahram Shah Heydari and Khalil El-Khatib
Additional contact information
Fateme Mazloomi: Faculty of Business and IT, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada
Shahram Shah Heydari: Faculty of Business and IT, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada
Khalil El-Khatib: Faculty of Business and IT, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada

Future Internet, 2025, vol. 17, issue 7, 1-31

Abstract: Federated learning (FL) has emerged as a powerful approach for privacy-preserving model training in autonomous vehicle networks, where real-world deployments rely on multiple roadside units (RSUs) serving heterogeneous clients with intermittent connectivity. While most research focuses on single-server or hierarchical cloud-based FL, multi-server FL can alleviate the communication bottlenecks of traditional setups. To this end, we propose an edge-based, multi-server FL (MS-FL) framework that combines performance-driven aggregation at each server—including statistical weighting of peer updates and outlier mitigation—with an application layer handover protocol that preserves model updates when vehicles move between RSU coverage areas. We evaluate MS-FL on both MNIST and GTSRB benchmarks under shard- and Dirichlet-based non-IID splits, comparing it against single-server FL and a two-layer edge-plus-cloud baseline. Over multiple communication rounds, MS-FL with the Statistical Performance-Aware Aggregation method and Dynamic Weighted Averaging Aggregation achieved up to a 20-percentage-point improvement in accuracy and consistent gains in precision, recall, and F1-score (95% confidence), while matching the low latency of edge-only schemes and avoiding the extra model transfer delays of cloud-based aggregation. These results demonstrate that coordinated cooperation among servers based on model quality and seamless handovers can accelerate convergence, mitigate data heterogeneity, and deliver robust, privacy-aware learning in connected vehicle environments.

Keywords: federated learning; multi-server; mobility; hand over; aggregation; vehicular edge computing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/7/315/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/7/315/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:7:p:315-:d:1705292

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().