Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client
<|reference_start|>Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client: Although Federated Learning (FL) is promising in knowledge sharing for heterogeneous Artificial Intelligence of Thing (AIoT) devices, their training performance and energy efficacy are severely restricted in practical battery-driven scenarios due to the ``wooden barrel effect'' caused by the mismatch between homogeneous model paradigms and heterogeneous device capability. As a result, due to various kinds of differences among devices, it is hard for existing FL methods to conduct training effectively in energy-constrained scenarios, such as battery constraints of devices. To tackle the above issues, we propose an energy-aware FL framework named DR-FL, which considers the energy constraints in both clients and heterogeneous deep learning models to enable energy-efficient FL. Unlike Vanilla FL, DR-FL adopts our proposed Muti-Agents Reinforcement Learning (MARL)-based dual-selection method, which allows participated devices to make contributions to the global model effectively and adaptively based on their computing capabilities and energy capacities in a MARL-based manner. Experiments conducted with various widely recognized datasets demonstrate that DR-FL has the capability to optimize the exchange of knowledge among diverse models in large-scale AIoT systems while adhering to energy limitations. Additionally, it improves the performance of each individual heterogeneous device's model.<|reference_end|>
@article{xia2024towards,
    title={Towards Energy-Aware Federated Learning via MARL: A Dual-Selection
  Approach for Model and Client},
    author={Jun Xia and Yi Zhang and Yiyu Shi},
    journal={arXiv preprint arXiv:2405.08183},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08183},
    primaryClass={cs.LG cs.AI}
}