Experience-Based Participant Selection in Federated Reinforcement Learning for Edge Intelligence
Chae‐Eun Lee, Woonghee Lee · IEEE Computational Intelligence Magazine · 2025
Currently, federated reinforcement learning (FRL) is driving progress in the edge artificial intelligence (AI) sector by effectively addressing a range of environmental challenges through distributed learning. The selection of participants in federated learning (FL) is especially vital because it greatly influences the overall performance and learning efficiency of the system. However, unlike traditional FL, the absence of a pre-accumulated dataset in FRL makes current FL participant selection methods unsuitable for FRL systems. To address this limitation, this paper proposes a novel experience-based participant selection method, tailored for FRL systems. This approach uses dimensionality reduction techniques to effectively analyze the experiences of agents and employs similarity measurements to intelligently select agents with diverse experiences. The FRL system has been implemented using the proposed technique, and extensive experiments have been performed to assess the system’s performance from various perspectives. Our evaluation results show a considerable increase in the intelligence of the FRL system, resulting in more efficient training and an overall enhancement in performance. This is demonstrated by a considerable reduction of 23.65% in average training time and an average performance improvement of 53.25% compared to existing methods.