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

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 cur...

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Bibliographic Details
Published in:IEEE transactions on green communications and networking 2024-12, Vol.8 (4), p.1862-1874
Main Authors: Koursioumpas, Nikolaos, Magoula, Lina, Petropouleas, Nikolaos, Thanopoulos, Alexandros-Ioannis, Panagea, Theodora, Alonistioti, Nancy, Gutierrez-Estevez, M. A., Khalili, Ramin
Format: Article
Language:English
Subjects:
Beyond 5G
Computational modeling
Costs
Energy consumption
Energy efficiency
federated learning
Performance evaluation
reinforcement learning
Resource management
Training
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Summary: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 contributing towards 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 and robustness of the proposed scheme compared to four state-of-the-art baseline solutions on different network environments and FL architectures, achieving a decrease of up to 94% in the total energy consumption.
ISSN:2473-2400
2473-2400
DOI:10.1109/TGCN.2024.3372695