Efficient Post-Quantum Cross-Silo Federated Learning Based on Key Homomorphic Pseudo-Random Function

Federated Learning (FL) enables collaborative model training across distributed users, while preserving data privacy by only sharing model updates. However, secure aggregation, which is essential to prevent data leakage during this process, often incurs significant communication and computational costs. Moreover, existing schemes rarely consider whether they can resist quantum attacks. To address these challenges, we propose an efficient, post-quantum aggregation protocol based on a Key Homomorphic Pseudo-Random Function (KHPRF). Our non-interactive mask elimination mechanism reduces aggregation to a single round, significantly minimizing the communication overhead. Furthermore, the KHPRF keys are reusable, enabling multiple aggregations with a one-time initialization, thereby enhancing efficiency in cross-silo federated learning. Compared to existing schemes, our approach achieves quantum-resistant aggregation with improved efficiency.