Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client

• URL: http://arxiv.org/abs/2405.08183v2
• Date: Tue, 9 Jul 2024 16:46:19 GMT
• Title: Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client
• Authors: Jun Xia, Yi Zhang, Yiyu Shi,
• Abstract summary: We propose an energy-aware Federated Learning (FL) framework named DR-FL. DR-FL 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.
• Score: 16.67119399590236
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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