New Framework Tackles Multi-Model Training Challenges in Vehicle-Edge-Cloud Federated Learning

Global: New Framework Tackles Multi-Model Training Challenges in Vehicle-Edge-Cloud Federated Learning

Researchers announced a novel framework on arXiv on January 31, 2025, aimed at improving hierarchical federated learning across vehicle‑edge‑cloud (VEC) architectures. The work addresses the difficulty of training multiple machine‑learning models simultaneously on highly mobile vehicles, a scenario that can cause model obsolescence, inefficient data use, and unbalanced resource allocation. By targeting reduced global training latency while maintaining task balance, the authors seek to enhance collaborative learning in the rapidly expanding Internet of Vehicles ecosystem.

Background and Motivation

The proliferation of AI‑enabled Internet of Vehicles (IoV) has intensified the need for scalable, decentralized learning solutions. Traditional VEC‑HFL approaches often assume a single model per vehicle, overlooking the reality that modern automotive platforms must support diverse applications such as perception, navigation, and predictive maintenance concurrently. This multi‑model environment introduces three primary challenges: (1) aggregation rules that can render models outdated, (2) vehicular mobility that hampers timely model uploads to edge servers, and (3) the necessity of equitable resource distribution among tasks.

Hybrid Synchronous‑Asynchronous Aggregation

To mitigate the first two challenges, the authors propose a hybrid aggregation rule that blends synchronous updates—ensuring consistency for critical tasks—with asynchronous updates—allowing vehicles that are out of range to continue training without stalling the overall process. This dual mode is designed to keep models current while accommodating the high mobility inherent in vehicular networks.

HEART Methodology

The core of the framework, named Hybrid Evolutionary And gReedy allocaTion (HEART), operates in two stages. The initial stage employs a hybrid heuristic that merges an improved Particle Swarm Optimization (PSO) algorithm with Genetic Algorithms (GA) to generate balanced task‑scheduling plans across the VEC hierarchy. In the second stage, a low‑complexity greedy algorithm assigns training priorities to the tasks allocated to each vehicle, ensuring that limited computational resources are used efficiently.

Experimental Evaluation

Using real‑world vehicular datasets, the authors benchmark HEART against several state‑of‑the‑art baselines. Results indicate that HEART consistently lowers overall training latency and achieves more uniform task completion times, confirming the superiority of the proposed hybrid approach in dynamic VEC‑HFL settings.

Implications and Future Work

The study highlights the feasibility of multi‑model federated learning in highly mobile environments and suggests that hybrid aggregation combined with evolutionary heuristics can address scalability concerns. Future research directions include extending the framework to incorporate privacy‑preserving mechanisms and testing its robustness under varying network conditions.

Conclusion

By formulating the multi‑model training problem as NP‑hard and delivering a practical heuristic solution, the authors contribute a significant step toward operationalizing collaborative AI across connected vehicles. Their findings may inform both academic investigations and industry deployments seeking to harness the full potential of AI in the Internet of Vehicles.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.