SFedRec: A Federated Learning Framework for Dynamic Session-based Recommendation

Session-based recommendation systems are critical for capturing users' evolving interests in real-time interactions. However, applying such systems in a federated learning (FL) setting presents challenges related to decentralized data and privacy preservation. To address this, we propose SFedRec, a session-based federated recommendation framework that integrates long-term user preferences with dynamicd session-based behaviors. SFedRec builds decentralized heterogeneous knowledge graphs to model user-item interactions and social connections, utilizing a graph neural network to learn user representations while ensuring privacy through Local Differential Privacy (LDP). Extensive experiments on three real-world datasets demonstrate that SFedRec outperforms stateof-the-art federated recommendation models, showing significant improvements in both general and cold-start scenarios.