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

Garland is a system for recommendation engines that use graph neural networks (GNNs, which are AI models that learn patterns from interconnected user-item relationships) in federated settings, where data stays on users' devices instead of being sent to one central server. The system addresses a key problem: untrusted servers that help expand users' local data can spy on both item information and user relationships, so Garland uses secret-shared shuffle (a cryptographic technique that mixes data while keeping it encrypted) to protect privacy while still catching if a malicious server tries to cheat.