Privacy-Preserving Cross-Domain Personalization: Leveraging E-commerce Behavior for Adaptive E-learning Pathways using Federated Graph Networks

This paper introduces FedGraph-LearnEd, a novel framework addressing the challenge of personalizing E-learning pathways by leveraging user behavioral data from E-commerce, while preserving user privacy. Traditional personalization methods face data sparsity issues [1], and existing Cross-Domain Recommendation (CDR) techniques often rely on overlapping users or fail to handle domain heterogeneity and privacy concerns effectively, especially between commercial and educational contexts [2] [3]. FedGraph-LearnEd utilizes a Federated Learning (FL) architecture combined with Graph Neural Networks (GNNs) to enable knowledge transfer without centralizing raw user data or requiring direct user overlap. The framework incorporates sequence modeling for E-commerce behavior, GNNs for E-learning structures, domain adaptation techniques for data fusion, and mechanisms to mitigate negative transfer, ensuring pedagogical relevance [8]. Key contributions include the privacy-preserving federated GNN architecture, methods for handling heterogeneous data, a focus on pedagogically-aware transfer, and consideration of ethical challenges like bias mitigation. FedGraph-LearnEd aims to enhance E-learning personalization, particularly in cold-start scenarios, by securely utilizing E-commerce signals to recommend relevant learning paths and resources.