# A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks

arXiv: https://arxiv.org/abs/2306.14237 · HTML: https://mlsanity.com/paper/2306.14237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks](https://mlsanity.com/paper/2308.10664/) (2023)
- [Federated Learning of Low-Rank One-Shot Image Detection Models in Edge Devices with Scalable Accuracy and Compute Complexity](https://mlsanity.com/paper/2504.16515/) (2025)

## Abstract (arXiv)

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.