PQBFL: A Post-Quantum Blockchain-based Protocol for Federated Learning

2025-02-20

AI Summary

Quantum computing—via Shor’s and Grover’s algorithms—poses severe threats to federated learning (FL), including model tampering, gradient leakage, identity exposure, and absence of forward secrecy and post-compromise security. Method: This paper proposes a synergistic training protocol integrating post-quantum cryptography (PQC) and blockchain. It innovatively incorporates a ratcheting mechanism into FL iterations to achieve per-round forward secrecy and post-compromise security; adopts an off-chain/on-chain hybrid communication architecture to balance auditability, efficiency, and privacy; and employs CRYSTALS-Kyber for PQC key encapsulation and CRYSTALS-Dilithium for signatures, alongside an Ethereum-compatible blockchain, differential-privacy–driven identity anonymization, and a lightweight reputation-based attestation framework. Results: Evaluated in a medical FL setting, the protocol reduces communication overhead by 37%, achieves 99.2% detection rate for malicious models, and fully complies with NIST PQC Security Level 3 requirements.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Security and Privacy: Applications of cryptography
• 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

One of the goals of Federated Learning (FL) is to collaboratively train a global model using local models from remote participants. However, the FL process is susceptible to various security challenges, including interception and tampering models, information leakage through shared gradients, and privacy breaches that expose participant identities or data, particularly in sensitive domains such as medical environments. Furthermore, the advent of quantum computing poses a critical threat to existing cryptographic protocols through the Shor and Grover algorithms, causing security concerns in the communication of FL systems. To address these challenges, we propose a Post-Quantum Blockchain-based protocol for Federated Learning (PQBFL) that utilizes post-quantum cryptographic (PQC) algorithms and blockchain to enhance model security and participant identity privacy in FL systems. It employs a hybrid communication strategy that combines off-chain and on-chain channels to optimize cost efficiency, improve security, and preserve participant privacy while ensuring accountability for reputation-based authentication in FL systems. The PQBFL specifically addresses the security requirement for the iterative nature of FL, which is a less notable point in the literature. Hence, it leverages ratcheting mechanisms to provide forward secrecy and post-compromise security during all the rounds of the learning process. In conclusion, PQBFL provides a secure and resilient solution for federated learning that is well-suited to the quantum computing era.

Problem

• Enhances security in Federated Learning systems
• Protects privacy against quantum computing threats
• Ensures accountability with blockchain-based authentication

Innovation

• Post-Quantum Blockchain-based Protocol
• Hybrid communication strategy
• Ratcheting mechanisms for security