  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.
