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

arXiv: https://arxiv.org/abs/2507.06997 · HTML: https://mlsanity.com/paper/2507.06997/

## 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 (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Clustered Vehicular Federated Learning: Process and Optimization](https://mlsanity.com/paper/2201.11271/) (2022)
- [Security and privacy for 6G: A survey on prospective technologies and challenges](https://mlsanity.com/paper/2108.11861/) (2021)
- [Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services](https://mlsanity.com/paper/2202.06435/) (2022)
- [Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles](https://mlsanity.com/paper/2201.11295/) (2022)
- [Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks](https://mlsanity.com/paper/2112.03465/) (2021)
- [A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks](https://mlsanity.com/paper/2201.11831/) (2022)
- [Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning](https://mlsanity.com/paper/2505.14581/) (2025)
- [Towards a Secure and Reliable Federated Learning using Blockchain](https://mlsanity.com/paper/2201.11311/) (2022)
- [What Physical Layer Security Can Do for 6G Security](https://mlsanity.com/paper/2212.00427/) (2022)

## Built on by (most cited first, 5 of 5)

- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks](https://mlsanity.com/paper/2604.01238/) (2026)
- [Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery](https://mlsanity.com/paper/2604.01049/) (2026)
- [When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System](https://mlsanity.com/paper/2605.20037/) (2026)
- [Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach](https://mlsanity.com/paper/2606.14515/) (2026)

## 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.