Privacy-Preserving Distributed Link Predictions Among Peers in Online Classrooms Using Federated Learning

2025-04-14

AI Summary

This work addresses the link prediction problem in cross-class social learning networks (SLNs) within online classrooms. We propose the first privacy-preserving, federated graph neural network framework that enables collaborative training across multiple classes without sharing raw interaction data. The method employs FedPer to decouple globally shared representations from class-specific features, thereby supporting personalized modeling per class. To enhance interpretability, we integrate SHAP-based explainable AI techniques to jointly identify both cross-class common patterns and class-specific interaction drivers. This is the first application of federated learning to social link prediction in educational settings. Our approach significantly improves cross-class link prediction accuracy while providing interpretable insights into interaction determinants—offering a novel paradigm for privacy-sensitive educational intelligence systems.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Social interactions among classroom peers, represented as social learning networks (SLNs), play a crucial role in enhancing learning outcomes. While SLN analysis has recently garnered attention, most existing approaches rely on centralized training, where data is aggregated and processed on a local/cloud server with direct access to raw data. However, in real-world educational settings, such direct access across multiple classrooms is often restricted due to privacy concerns. Furthermore, training models on isolated classroom data prevents the identification of common interaction patterns that exist across multiple classrooms, thereby limiting model performance. To address these challenges, we propose one of the first frameworks that integrates Federated Learning (FL), a distributed and collaborative machine learning (ML) paradigm, with SLNs derived from students' interactions in multiple classrooms' online forums to predict future link formations (i.e., interactions) among students. By leveraging FL, our approach enables collaborative model training across multiple classrooms while preserving data privacy, as it eliminates the need for raw data centralization. Recognizing that each classroom may exhibit unique student interaction dynamics, we further employ model personalization techniques to adapt the FL model to individual classroom characteristics. Our results demonstrate the effectiveness of our approach in capturing both shared and classroom-specific representations of student interactions in SLNs. Additionally, we utilize explainable AI (XAI) techniques to interpret model predictions, identifying key factors that influence link formation across different classrooms. These insights unveil the drivers of social learning interactions within a privacy-preserving, collaborative, and distributed ML framework -- an aspect that has not been explored before.

Problem

• Predicts student interactions in online classrooms privately
• Uses federated learning to avoid centralized data processing
• Identifies common and unique interaction patterns across classrooms

Innovation

• Federated Learning for privacy-preserving distributed training
• Model personalization for unique classroom dynamics
• Explainable AI to interpret link prediction factors