[论文解读] APPFLChain: A Privacy Protection Distributed Artificial-Intelligence Architecture Based on Federated Learning and Consortium Blockchain

Jun-Teng Yang, Wen‐Yuan Chen+3 more|arXiv (Cornell University)|Jun 26, 2022

Privacy-Preserving Technologies in Data被引用 5

一句话总结

本文提出 APPFLChain，一种结合基于 Hyperledger Fabric 的联盟区块链与联邦学习的隐私保护分布式人工智能架构，以实现安全、去中心化的模型训练。通过消除集中式数据收集，该系统确保了数据隐私、防篡改性、可追溯性以及可靠的模型推理，仿真结果证实其在保持安全性和完整性方面的有效性。

ABSTRACT

Recent research in Internet of things has been widely applied for industrial practices, fostering the exponential growth of data and connected devices. Henceforth, data-driven AI models would be accessed by different parties through certain data-sharing policies. However, most of the current training procedures rely on the centralized data-collection strategy and a single computational server. However, such a centralized scheme may lead to many issues. Customer data stored in a centralized database may be tampered with so the provenance and authenticity of data cannot be justified. Once the aforementioned security concerns occur, the credibility of the trained AI models would be questionable and even unfavorable outcomes might be produced at the test stage. Lately, blockchain and AI, the two core technologies in Industry 4.0 and Web 3.0, have been explored to facilitate the decentralized AI training strategy. To serve on this very purpose, we propose a new system architecture called APPFLChain, namely an integrated architecture of a Hyperledger Fabric-based blockchain and a federated-learning paradigm. Our proposed new system allows different parties to jointly train AI models and their customers or stakeholders are connected by a consortium blockchain-based network. Our new system can maintain a high degree of security and privacy as users do not need to share sensitive personal information to the server. For numerical evaluation, we simulate a real-world scenario to illustrate the whole operational process of APPFLChain. Simulation results show that taking advantage of the characteristics of consortium blockchain and federated learning, APPFLChain can demonstrate favorable properties including untamperability, traceability, privacy protection, and reliable decision-making.

研究动机与目标

• 解决物联网和工业 4.0 系统中集中式人工智能训练所面临的隐私与安全风险。
• 克服单服务器数据收集模式的局限性，包括数据篡改和缺乏可追溯性。
• 在不共享原始数据的前提下，实现多方之间的协作式人工智能模型训练。
• 通过可验证、不可篡改且可追溯的训练过程，确保模型的可信度。
• 为工业应用开发一种去中心化、可信且隐私保护的人工智能训练框架。

提出的方法

• 设计一种混合架构，结合基于 Hyperledger Fabric 的联盟区块链与联邦学习，实现去中心化人工智能训练。
• 采用联邦学习技术，使客户端能够在不将原始数据传输至中心服务器的情况下训练本地模型。
• 利用区块链记录模型更新、聚合结果和训练元数据，以实现可审计性和完整性。
• 在联盟区块链中实施访问控制和共识机制，以确保参与方之间的信任。
• 通过不可篡改的账本条目，实现模型更新和数据可追溯性的支持。
• 集成密码学技术，保护模型参数并在通信过程中维护隐私。

实验结果

研究问题

• RQ1 去中心化人工智能训练系统如何在保障模型准确性和可信度的同时，确保数据隐私？
• RQ2 联盟区块链在多大程度上能够提升工业物联网环境中联邦学习的安全性和可追溯性？
• RQ3 区块链与联邦学习的集成能否有效降低数据篡改和未授权访问的风险？
• RQ4 所提出的架构如何在多方环境中支持可靠且可验证的人工智能决策？
• RQ5 该系统在真实世界仿真场景下的实际性能特征如何？

主要发现

• APPFLChain 有效消除了对集中式数据存储的需求，显著降低了隐私与安全风险。
• 通过不可篡改的区块链记录模型更新和训练事件，系统确保了数据的可追溯性与真实性。
• 仿真结果证实，该系统能够维持数据完整性，并在训练过程中防止数据篡改。
• 该架构支持对模型贡献的可追溯性，从而在参与各方之间实现问责与可审计性。
• 联邦学习与联盟区块链的集成增强了隐私保护，同时保持了模型的可靠性。
• 系统在真实世界仿真中表现出色，具备防篡改性、可追溯性和隐私保护等优良特性。