Federated Learning and Blockchain Integration for Privacy-Preserving Smart Classrooms

Abstract

The development of smart classroom technology hastened the implementation of data-driven and personalized learning systems, with the significant concern of data privacy, security, and trust. In this chapter, the author introduces a combined system of Federated Learning (FL) and blockchain to implement decentralized and privacy-sensitive smart classrooms. FL enables distributed nodes to collaboratively train models without exchanging raw student data, which is guaranteed to preserve the privacy of student data. Blockchain improves the reliability of the system by offering a secure, transparent and non-tamperable model updating and interaction validation. The paper contains a conceptual model, methodology, and case-based analysis with the evidence of privacy, accuracy, and scalability of the system improvement. The framework suggested will facilitate safe, effective and reliable learning ecosystems of the next generation smart learning classrooms.