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

• URL: http://arxiv.org/abs/2502.14464v1
• Date: Thu, 20 Feb 2025 11:36:08 GMT
• Title: PQBFL: A Post-Quantum Blockchain-based Protocol for Federated Learning
• Authors: Hadi GHaravi, Jorge Granjal, Edmundo Monteiro,
• Abstract summary: We propose a Post-Quantum-based protocol for Federated Learning (PQBFL) to enhance model security and participant identity privacy in FL systems.<n>It employs a hybrid communication strategy that combines off-chain and on-chain channels to optimize cost efficiency, improve security, and preserve participant privacy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Information-Theoretic Decentralized Secure Aggregation with Collusion Resilience [98.31540557973179]
  We study the problem of decentralized secure aggregation (DSA) from an information-theoretic perspective.<n>We characterize the optimal rate region, which specifies the minimum achievable communication and secret key rates for DSA.<n>Our results establish the fundamental performance limits of DSA, providing insights for the design of provably secure and communication-efficient protocols.
  arXiv  Detail & Related papers  (2025-08-01T12:51:37Z)
• Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security [17.45950557331482]
  Federated learning (FL) enables collaborative model training while preserving user data privacy by keeping data local.<n>Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment.<n>We introduce Beskar, a novel framework that provides post-quantum secure aggregation.
  arXiv  Detail & Related papers  (2025-05-09T03:20:48Z)
• VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning [2.90114256542208]
  This work presents VerifBFL, a trustless, privacy-preserving, and verifiable federated learning framework. VerifBFL ensures the verifiability of both local training and aggregation processes. To protect training data from inference attacks, VerifBFL leverages differential privacy.
  arXiv  Detail & Related papers  (2025-01-08T07:32:54Z)
• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• Voltran: Unlocking Trust and Confidentiality in Decentralized Federated Learning Aggregation [12.446757264387564]
  We present Voltran, an innovative hybrid platform designed to achieve trust, confidentiality, and robustness for Federated Learning (FL) We offload the FL aggregation into TEE to provide an isolated, trusted and customizable off-chain execution. We provide strong scalability on multiple FL scenarios by introducing a multi-SGX parallel execution strategy.
  arXiv  Detail & Related papers  (2024-08-13T13:33:35Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions [31.18229828293164]
  Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability.
  arXiv  Detail & Related papers  (2024-03-01T07:41:05Z)
• Foundations of Quantum Federated Learning Over Classical and Quantum Networks [59.121263013213756]
  Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical federated learning (FL) with the computational power of quantum technologies. QFL can be deployed over both classical and quantum communication networks.
  arXiv  Detail & Related papers  (2023-10-23T02:56:00Z)
• A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing [0.8029049649310213]
  Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence. The performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry.
  arXiv  Detail & Related papers  (2023-03-24T00:40:08Z)
• FedComm: Federated Learning as a Medium for Covert Communication [56.376997104843355]
  Federated Learning (FL) is a solution to mitigate the privacy implications related to the adoption of deep learning. This paper thoroughly investigates the communication capabilities of an FL scheme. We introduce FedComm, a novel multi-system covert-communication technique.
  arXiv  Detail & Related papers  (2022-01-21T17:05:56Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients [124.48732110742623]
  We propose a novel framework by integrating blockchain into Federated Learning (FL) BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning. It gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors.
  arXiv  Detail & Related papers  (2020-12-02T12:18:27Z)
• Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach [54.29213445674221]
  Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO) The issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency. We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for theO.
  arXiv  Detail & Related papers  (2020-04-08T16:29:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.