A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks

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Nikolaos Koursioumpas, Lina Magoula, Nikolaos Petropouleas, Alexandros-Ioannis Thanopoulos, Theodora Panagea, Nancy Alonistioti, M. A. Gutierrez-Estevez, Ramin Khalili


Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia. Federated Learning (FL) has emerged as a key privacy preserving decentralized AI technique. Despite efforts currently being made in FL, its environmental impact is still an open problem. Targeting the minimization of the overall energy consumption of an FL process, we propose the orchestration of computational and communication resources of the involved devices to minimize the total energy required, while guaranteeing a certain performance of the model. To this end, we propose a Soft Actor Critic Deep Reinforcement Learning (DRL) solution, where a penalty function is introduced during training, penalizing the strategies that violate the constraints of the environment, and ensuring a safe RL process. A device level synchronization method, along with a computationally cost effective FL environment are proposed, with the goal of further reducing the energy consumption and communication overhead. Evaluation results show the effectiveness of the proposed scheme compared to four state-of-the-art baseline solutions in both static and dynamic environments, achieving a decrease of up to 94% in the total energy consumption.

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