  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.
