Federated Learning in Education: Enhancing Student Privacy in AI-Based Feedback Mechanisms
Abstract
Federated Learning (FL) offers a groundbreaking approach to developing AI models in educational settings by preserving data privacy while enabling personalized learning experiences. By keeping sensitive student data local to their devices, FL mitigates the risks associated with traditional centralized systems, ensuring compliance with stringent privacy regulations such as the GDPR and the FERPA. This book chapter explores the integration of Federated Learning into educational contexts, with a particular focus on adaptive learning systems, privacy-enhancing mechanisms, and the ethical and legal considerations crucial for successful implementation. The role of model aggregation, balancing performance with privacy concerns, was critically analyzed, alongside the impact of Federated Learning on personalized education, student data security, and system scalability. Key challenges in data heterogeneity and ensuring fairness in AI models are also discussed, highlighting strategies for overcoming these obstacles. By addressing both the technical and regulatory aspects, this chapter provides a comprehensive overview of the potential of Federated Learning to revolutionize education while ensuring that privacy, equity, and legal compliance remain paramount.
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