Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks
This work addresses security challenges for legitimate users in multi-cellular networks, presenting an incremental improvement by combining federated learning with existing reinforcement learning methods.
This paper tackles the problem of enhancing physical-layer security in beyond 5G networks by applying a federated learning-based multi-agent reinforcement learning strategy, showing that the Reinforce deep policy gradient method converges more rapidly than deep Q-network and outperforms distributed DRL approaches.
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.