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.

翻译：迈向人工智能赋能的无线网络新时代之际，产业界与学术界对AI的环境影响日益关注。联邦学习作为保护隐私的关键去中心化AI技术而兴起。尽管目前联邦学习领域已有诸多努力，但其环境影响仍是一个未解难题。针对联邦学习过程中总体能耗最小化的问题，我们提出通过协调参与设备的计算与通信资源，在保证模型性能的前提下最小化总能耗。为此，我们提出一种基于软演员-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，确保强化学习过程的安全性。同时提出设备级同步方法与计算成本效益高的联邦学习环境，旨在进一步降低能耗与通信开销。评估结果表明，与四种静态及动态环境下的最新基线方案相比，所提方案可将总能耗降低高达94%。