Construction of a federated learning-based privacy-preserving and intelligent recommendation model for educational data

With the development of the advanced educational platform, more and more personalized recommendation systems are used to increase the efficiency of learning and make the dissemination of the educational resources easier. However, educational recommendation tasks involve highly sensitive student data. The current centralized recommendation methods have limitations in learning diverse learner preferences across institutions and are susceptible to privacy violations.The existing federated recommendation methods mainly focus on the recommendation effectiveness or privacy protection. However, few works consider privacy protection, sequential learner behavior modeling and personalized federated optimization in a unified framework. To tackle these challenges, in this work we propose a privacy preserving personalized federated recommendation system for educational data.The study propose a framework that incorporates differential privacy perturbation, personalized federated aggregation, sequential interest modeling, and context-aware ranking into the optimization objective. Local learner representation is learned with differential privacy constraints. Besides, sequential behavior encoding and attention-driven modeling are used to understand the dynamic interests of learners and the continuous relation of behaviors. Besides, we present a local-global representation fusion strategy to address the dilemma between learner customization and global generalization for non-independent and identically distributed (non-IID) federated scenarios.Extensive experiments under the IID, non-IID and privacy-preserving federated frameworks are performed on the MovieLens-1 M and Amazon Beauty datasets. Our results show that the proposed Differential Privacy Federated Averaging (DP-FedAvg) framework can effectively address the performance degradation problem caused by the diverse client distributions, and maintain the recommendation accuracy under different privacy budgets. In the MovieLens-1 M dataset, the DP-FedAvg achieves an average HR@10 of 0.710 and an average Normalized Discounted Cumulative Gain (NDCG@10) of 0.681, outperforming the usual non-IID federated benchmarks. The privacy-utility evaluation further shows a nice coexistence of the privacy protection and the usefulness of recommendations. We propose a framework that provides a unified and repeatable methodology for privacy-aware federated recommendations in smart educational environments in a decentralized manner.