Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. Towards mitigating the carbon footprint of FL, the current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization, by orchestrating the computational and communication resources of the involved devices, while guaranteeing a certain FL model performance target. A penalty function is introduced in the offline phase of the GA that penalizes the strategies that violate the constraints of the environment, ensuring a safe GA process. Evaluation results show the effectiveness of the proposed scheme compared to two state-of-the-art baseline solutions, achieving a decrease of up to 83% in the total energy consumption.

翻译：联邦学习（FL）已发展成为一种去中心化技术，与传统集中式方法相反，设备以协作方式执行模型训练，同时保护数据隐私。尽管在FL方面已有诸多努力，但其环境影响仍在研究中，因为已识别出若干影响其在无线网络中适用性的关键挑战。为减少FL的碳足迹，当前工作提出了一种遗传算法（GA）方法，旨在通过协调所涉及设备的计算与通信资源，在保证特定FL模型性能目标的前提下，最小化FL过程的整体能耗及不必要的资源利用。在GA的离线阶段引入了一个惩罚函数，该函数对违反环境约束的策略进行惩罚，确保GA过程的安全。评估结果表明，与两种最先进的基线解决方案相比，所提方案在总能耗上实现了高达83%的降低。