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

Abstract

One of Federated Learning’s (FL) goals is to collaboratively train a global model using local models from remote participants to ensure security and privacy. 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. Experiments demonstrate that the computational cost is $O\left(n^2\right)$ for all rounds and the communication complexity is $O(n+m)$ in hybrid communication settings. Compared to existing methods, the proposed scheme achieves superior performance in terms of data size and gas consumption on the blockchain. These results highlight that PQBFL provides a secure, efficient, and feasible protocol for federated learning environments in the era of quantum computing.