Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Mathematics

DOI: 10.3390/math13091404

Authors: Xiaoyuan Qin, Rui Xu

Institution: China University of Geosciences

Language: English

Topic: Cryptography and Data Security

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China