PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework

2025-05-03

AI Summary

Quantum computing poses a critical threat to the cryptographic foundations of traditional federated learning (FL), undermining confidentiality and integrity in collaborative model training. Method: This paper proposes the first quantum-resistant FL security framework integrating post-quantum cryptography (PQC) with blockchain. It deeply embeds ML-DSA-65—the NIST FIPS 204 standard candidate—into a smart-contract-driven FL system to enable quantum-safe digital signatures for model updates and decentralized verification. A lightweight consensus mechanism and an optimized secure aggregation protocol are designed to ensure long-term cryptographic resilience without compromising real-time performance. Results: Experiments show ML-DSA-65 signature and verification average 0.65 ms and 0.53 ms, respectively, with PQC overhead仅为 0.01–0.02%; MNIST test accuracy reaches 98.8%, and average blockchain transaction latency is 4.8 s. This work demonstrates, for the first time, that PQC is not a performance bottleneck in blockchain-based FL, establishing a practical, quantum-resistant collaborative learning paradigm for high-stakes domains such as healthcare.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Federated Learning (FL) enables collaborative model training while preserving data privacy, but its classical cryptographic underpinnings are vulnerable to quantum attacks. This vulnerability is particularly critical in sensitive domains like healthcare. This paper introduces PQS-BFL (Post-Quantum Secure Blockchain-based Federated Learning), a framework integrating post-quantum cryptography (PQC) with blockchain verification to secure FL against quantum adversaries. We employ ML-DSA-65 (a FIPS 204 standard candidate, formerly Dilithium) signatures to authenticate model updates and leverage optimized smart contracts for decentralized validation. Extensive evaluations on diverse datasets (MNIST, SVHN, HAR) demonstrate that PQS-BFL achieves efficient cryptographic operations (average PQC sign time: 0.65 ms, verify time: 0.53 ms) with a fixed signature size of 3309 Bytes. Blockchain integration incurs a manageable overhead, with average transaction times around 4.8 s and gas usage per update averaging 1.72 x 10^6 units for PQC configurations. Crucially, the cryptographic overhead relative to transaction time remains minimal (around 0.01-0.02% for PQC with blockchain), confirming that PQC performance is not the bottleneck in blockchain-based FL. The system maintains competitive model accuracy (e.g., over 98.8% for MNIST with PQC) and scales effectively, with round times showing sublinear growth with increasing client numbers. Our open-source implementation and reproducible benchmarks validate the feasibility of deploying long-term, quantum-resistant security in practical FL systems.

Problem

• Secures Federated Learning against quantum attacks
• Integrates post-quantum cryptography with blockchain verification
• Maintains model accuracy while ensuring quantum-resistant security

Innovation

• Integrates post-quantum cryptography with blockchain
• Uses ML-DSA-65 signatures for authentication
• Optimized smart contracts for decentralized validation