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.

翻译：步入人工智能赋能无线网络的新纪元，工业界与学术界对AI的环境影响问题日益关注。联邦学习作为一种保护隐私的分布式AI技术应运而生，尽管当前对其环境影响的研究有所进展，但仍是一个开放性问题。针对联邦学习过程的总能耗最小化目标，我们提出通过协调参与设备的计算与通信资源，在保证模型特定性能的前提下，实现总能耗最小化。为此，我们设计了一种基于软演员-评论家深度强化学习的解决方案，在训练过程中引入惩罚函数，对违反环境约束的策略进行惩罚，从而促进安全强化学习过程。此外，我们提出设备级同步方法与计算高效的联邦学习环境，进一步降低能耗与通信开销。评估结果表明，与四种先进基线方案相比，所提方案在不同网络环境和联邦学习架构下均展现出卓越效能与鲁棒性，总能耗最多可降低94%。