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：迈向人工智能赋能的无线网络新时代之际，工业界和学术界对人工智能的环境影响日益关注。联邦学习作为保护隐私的去中心化人工智能关键技术应运而生。尽管当前针对联邦学习已展开诸多努力，但其环境影响仍是一个待解决的开放问题。为最小化联邦学习过程的总体能耗，我们提出协同调度各设备计算与通信资源的方案，在保证模型特定性能的前提下降低总能耗。为此，我们设计了一种基于软演员-评论家的深度强化学习解决方案，通过引入训练过程中的惩罚函数，对违反环境约束的策略进行惩戒，促进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优化的联邦学习环境，旨在进一步降低能耗与通信开销。评估结果表明，与四种现有先进基线方案相比，本方案在不同网络环境和联邦学习架构下均展现出显著的有效性和鲁棒性，总能耗最高可降低94%。