PQBFL: A Post-Quantum Blockchain-based Protocol for Federated Learning
Hadi GHaravi, Jorge Granjal, Edmundo Monteiro

TL;DR
This paper introduces PQBFL, a blockchain-based federated learning protocol that employs post-quantum cryptography to secure model sharing and participant privacy against quantum threats, ensuring security and accountability.
Contribution
It proposes a novel post-quantum cryptographic blockchain protocol for federated learning, addressing security, privacy, and accountability in the quantum computing era.
Findings
Enhanced security against quantum attacks
Improved participant privacy through blockchain
Secure iterative federated learning with ratcheting mechanisms
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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Stochastic Gradient Optimization Techniques
