Garland: Graph Neural Network-Based Federated Recommendation With Malicious Security via Secret-Shared Shuffle

Summary

Garland is a federated recommendation scheme built on graph neural networks that addresses the limited one-order subgraphs each user can access. It expands subgraphs using secret-shared shuffle so that both item privacy and user relationship privacy are preserved, with integrity checks against malicious servers. The source states that theoretical analysis supports its privacy and integrity, and that experiments on four datasets show it outperforms state-of-the-art solutions.