Mitigating Data Sparsity and Privacy Risks in Educational Recommender System through Federated Learning

The research investigates secure recommender systems through federated learning on educational platforms because online education platforms face increasing threats to student data privacy. The research creates an innovative system which merges FL technology with collaborative filtering to generate personalised course recommendations while maintaining user data protection on client devices. The system evaluated its performance by analysing data from major platforms including edX and Coursera and Udemy and other platforms through MSE and R-squared and precision and recall and F1-score metrics. The evaluation shows that FL maintains user privacy through data aggregation restrictions but users must accept reduced recommendation quality than what centralised systems offer. The research establishes two essential findings which confirm FL maintains user privacy in secure educational settings and reveals that performance reduction from limited data constitutes a core challenge for distributed systems. The research presents two primary methodological contributions which integrate data preprocessing methods for dealing with missing information and develop a complete evaluation system for federated recommendation platforms. The research results differ from previous studies because they demonstrate how model performance deteriorates when operating under federated system constraints. The research develops educational technology FL application expertise by studying privacy-accuracy tradeoffs and presenting methods to boost federated recommender systems in protected data environments.