Post-Quantum Cryptography Secures Federated Learning with Blockchain Verification ∞ Research

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A faceted, transparent cube containing glowing blue circuit patterns dominates the foreground, evoking a quantum processing unit. The background is a soft focus of metallic and deep blue elements, suggestive of interconnected nodes within a distributed ledger system or secure hardware for cryptocurrency storage

Briefing

This research addresses the critical vulnerability of classical cryptographic systems in Federated Learning (FL) to quantum attacks, a threat particularly pronounced in sensitive domains like healthcare. It proposes PQS-BFL, a novel framework that integrates post-quantum cryptography (PQC) with blockchain verification to secure FL against quantum adversaries. This foundational breakthrough ensures the long-term resilience of collaborative machine learning infrastructures, enabling robust and quantum-resistant data privacy without compromising operational efficiency or model accuracy.

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Context

Before this research, Federated Learning (FL) offered a paradigm for collaborative model training while preserving data privacy by keeping raw data localized. However, the established cryptographic methods underpinning these FL systems, such as Elliptic Curve Cryptography (ECC), are demonstrably vulnerable to future quantum computing attacks, particularly Shor’s algorithm. This theoretical limitation presented a significant, unsolved challenge, risking the compromise of sensitive data and the integrity of collaboratively trained models in critical sectors, necessitating a shift towards quantum-resistant solutions.

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Analysis

The PQS-BFL framework introduces a core mechanism by integrating post-quantum cryptography (PQC) directly into the federated learning process, verified by a blockchain. This system fundamentally differs from previous approaches by replacing classical cryptographic primitives with quantum-resistant ones, specifically employing ML-DSA-65 (a FIPS 204 standard candidate, formerly Dilithium) signatures. When FL clients perform local model updates, these updates are authenticated using these PQC signatures.

Subsequently, optimized smart contracts on a blockchain facilitate decentralized validation of these signed updates, ensuring integrity and immutability. This architecture creates a robust, quantum-secure verifiable layer for collaborative model training, safeguarding against potential quantum breaches.

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Parameters

New System/Protocol ∞ PQS-BFL Framework
Core Cryptographic Primitive ∞ ML-DSA-65 (Dilithium) Signatures
Average PQC Sign Time ∞ 0.65 ms
Average PQC Verify Time ∞ 0.53 ms
Fixed Signature Size ∞ 3309 Bytes
Average Blockchain Transaction Time ∞ ~4.8 s
Average Gas Usage Per Update ∞ ~1.72 x 10^6 units
Model Accuracy (MNIST with PQC) ∞ Over 98.8%
Scalability ∞ Sublinear growth in round times with increasing client numbers
Key Authors ∞ D Commey, GV Crosby

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Outlook

This research opens new avenues for deploying quantum-resistant security in practical Federated Learning systems, particularly for organizations in security-critical sectors like healthcare. The demonstrated feasibility and manageable performance overhead of PQC within a blockchain context suggest that proactive upgrades to collaborative machine learning infrastructures are viable. In the next 3-5 years, this theory could unlock widespread adoption of quantum-secure FL, enabling highly sensitive data collaboration without sacrificing operational efficiency or model performance, thus future-proofing decentralized AI against emerging quantum threats.

PQS-BFL definitively establishes a practical and efficient blueprint for integrating post-quantum cryptography into blockchain-verified federated learning, fundamentally enhancing the long-term security and trustworthiness of decentralized AI systems.

Signal Acquired from ∞ arXiv.org