mlsanityData to 14 Oct 2026

Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks

arXiv 2507.06997 ↗First posted 9 Jul 2025eess.SP cs.ET cs.LGcited by 5

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Abstract (arXiv)

This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confidential information shared between the BS and its authorized users. The DRL agents are deemed to be federated since they only share their network parameters with a central server and not the private data of their legitimate users. Two DRL approaches, deep Q-network (DQN) and Reinforce deep policy gradient (RDPG), are explored and compared. The results demonstrate that RDPG converges more rapidly than DQN. In addition, we demonstrate that the proposed method outperforms the distributed DRL approach. Furthermore, the outcomes illustrate the trade-off between security and complexity.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 9 Jul 2025.

Builds on

13 of 30 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  3. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
  4. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  5. 2022Clustered Vehicular Federated Learning: Process and Optimizationcited by 15
  6. 2021Security and privacy for 6G: A survey on prospective technologies and challengescited by 12
  7. 2022Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Servicescited by 12
  8. 2022Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehiclescited by 7
  9. 2021Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networkscited by 6
  10. 2022A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networkscited by 6
  11. 2025Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learningcited by 5
  12. 2022Towards a Secure and Reliable Federated Learning using Blockchaincited by 4
1 more
  1. 2022What Physical Layer Security Can Do for 6G Securitycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunitiescited by 4
  2. 2026Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networkscited by 2
  3. 2026Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recoverycited by 0
  4. 2026When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control Systemcited by 0
  5. 2026Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approachcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
  2. 2025Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G
  3. 2026Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks
  4. 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directions
  5. 2025POLAR: Policy-based Layerwise Reinforcement Learning Method for Stealthy Backdoor Attacks in Federated Learning
  6. 2025Intelligent Attacks and Defense Methods in Federated Learning-enabled Energy-Efficient Wireless Networks
  7. 2025Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
  8. 2025Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization