HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

2025-01-17

AI Summary

To address model staleness, inefficient data utilization, and imbalanced resource allocation arising from concurrent multi-task training among highly mobile vehicles in vehicular networks, this paper proposes a vehicle-edge-cloud collaborative dynamic hierarchical federated learning framework. We design a novel hybrid synchronous-asynchronous aggregation mechanism to mitigate model aging. Furthermore, we formulate a two-stage scheduling framework—HEART—driven by an NP-hard optimization problem, integrating enhanced particle swarm optimization (PSO) and genetic algorithm (GA) for cross-task load balancing, and incorporate a lightweight greedy strategy to prioritize on-vehicle training tasks. Extensive experiments on real-world datasets demonstrate that our approach significantly reduces global training latency, accelerates model convergence, and improves cross-task training fairness. It consistently outperforms state-of-the-art vertical and hierarchical federated learning baselines in both efficiency and effectiveness.

Technology Category

• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks-a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA); second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods.

Problem

• Hierarchical Federated Learning
• Vehicular Networks
• Multi-task Resource Allocation

Innovation

• Hierarchical Federated Learning
• Multi-task Scheduling
• Hybrid Synchronization