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

TL;DR

This paper introduces DR-FL, an energy-aware federated learning framework utilizing MARL for dual-selection of devices and models, enhancing energy efficiency and performance in heterogeneous AIoT environments.

Contribution

It proposes a novel MARL-based dual-selection method for energy-aware federated learning, addressing device heterogeneity and energy constraints effectively.

Findings

DR-FL optimizes knowledge exchange among diverse models under energy limits.

It improves individual device model performance in AIoT systems.

Experimental results validate energy efficiency and effectiveness of DR-FL.

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.