Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies

New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independently on a server using distributed local client data.

The split learning method mitigates two fundamental drawbacks of federated learning: affordability, and privacy and security. When running mac...