HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning
Researchers have developed a new framework called HEART to address the challenges of multi-model training in Hierarchical Federated Learning (HFL) for vehicle-edge-cloud architectures. This framework aims to minimize global training latency and ensure balanced resource allocation across diverse tasks, which is a complex, NP-hard problem. HEART utilizes a hybrid synchronous-asynchronous aggregation rule and a two-stage approach involving evolutionary algorithms and a greedy method for task scheduling and prioritization. Experiments show HEART outperforms existing methods in dynamic VEC-HFL environments. AI
IMPACT This research could improve the efficiency and speed of AI model training in connected vehicle systems.