mlsanityData to 14 Oct 2026

A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks

arXiv 2308.10664 ↗First posted 21 Aug 2023cs.LG cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 47 references that are in the map. First posted on arXiv 21 Aug 2023.

Builds on

14 of 47 references.

References · 14

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 231
  6. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  7. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  8. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  9. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  10. 2022An Energy and Carbon Footprint Analysis of Distributed and Federated Learningcited by 16
  11. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  12. 2019Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approachcited by 6
2 more
  1. 2022Actor-Critic-Based Learning for Zero-touch Joint Resource and Energy Control in Network Slicingcited by 3
  2. 2023A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networkscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systemscited by 11
  2. 2026Federated Distributional Reinforcement Learning with Distributional Critic Regularizationcited by 1