To support privacy-preserving communications in wireless networks, GNNComm-MARL can be extended by incorporating techniques such as federated learning and physical layer security.
Federated Learning: This approach allows agents to train models locally and share only updated model parameters instead of raw data. By implementing federated learning in GNNComm-MARL, agents can collaborate without sharing sensitive information directly, thus enhancing privacy.
Physical Layer Security: Utilizing techniques like beamforming in cell-free mMIMO systems can enhance communication security by directing signals to specific users, reducing the risk of eavesdropping. By integrating physical layer security measures, GNNComm-MARL can ensure secure and private communication among agents.
By combining federated learning for distributed model training and physical layer security for secure communication, GNNComm-MARL can effectively support privacy-preserving communications in wireless networks.