Federated Learning for Privacy-Preserving IoT

The proliferation of IoT devices generates enormous volumes of sensitive data at the network edge. This project develops communication-efficient and privacy-preserving federated learning protocols that allow IoT devices to collaboratively train models without centralizing data, addressing both privacy regulations and bandwidth constraints inherent to IoT deployments.