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HEART framework tackles multi-model training for vehicle AI

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON This is a research paper detailing a new framework and algorithms for a specific AI training problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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HEART framework tackles multi-model training for vehicle AI

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This is a research paper detailing a new framework and algorithms for a specific AI training problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaohong Yang, Minghui Liwang, Xianbin Wang, Zhipeng Cheng, Seyyedali Hosseinalipour, Huaiyu Dai, Zhenzhen Jiao ·

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

    arXiv:2501.09934v3 Announce Type: replace-cross 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…

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