This project studies privacy-preserving federated learning over wireless networks. The work leverages the wireless medium itself as part of the learning and aggregation process, with the goal of improving communication efficiency, privacy, and robustness for distributed edge intelligence.
Research Focus
- Over-the-air model aggregation
- Privacy-preserving wireless federated learning
- Communication-efficient distributed AI
- Robust learning under wireless channel uncertainty
Related Publication
Publication:
Over-the-Air Federated Learning with Enhanced Privacy,
IEEE ICC, 2023.