Developing energy-efficient and privacy-preserving federated learning approaches for clients operating at the mobile edge.
Federated learning enables multiple devices to collaboratively train machine learning models without sharing their raw data — a crucial property in settings where privacy matters and data is sensitive. At the mobile edge, however, participating devices face severe constraints: limited battery life, unstable connectivity, and heterogeneous hardware.
FedCime, one of our contributions in this space, addresses the challenge of efficient federated learning for mobile edge clients by reducing communication overhead and adapting to varying client capabilities without sacrificing model quality.
We also apply federated learning to vehicular edge networks, where vehicles cooperate on tasks like task offloading decisions using deep reinforcement learning — enabling energy-efficient collaborative intelligence across a highly dynamic network topology.