Blockchain-based Federated Learning (BCFL) is gaining significant attention as a promising decentralized data sharing technology with privacy protection. Most existing BCFL frameworks loosely couple blockchain and Federated Learning (FL). FL transforms data sharing into model sharing, while blockchain decentralizes the model aggregation and handles security verification. However, this simplistic overlay of the two technologies often involves third-party blockchain peers, leading to inefficient workflows and potential privacy attacks from malicious blockchain peers. Some studies have proposed differential privacy with noise addition to prevent privacy attacks, yet most do not allow clients to customize privacy budgets, impacting data availability and limiting BCFL’s application in data sharing. To address these challenges, we first introduce a novel tightly-coupled BCFL framework that integrates training and mining at the client side. Under this framework, a totally decentralized data sharing process is established. Moreover, a Personalised Differential Privacy (PDP) mechanism is devised, enabling clients to add Laplace noise to model gradients based on custom privacy budgets. To achieve optimal privacy budgets, a privacy optimization mechanism based on Stackelberg games is proposed. It establishes utility functions for data requesters and providers, models the process of utility optimization as a Stackelberg game process, and obtains the optimal privacy budget while maximizing utility for all participants. This facilitates flexible and on-demand privacy-protected data sharing. Extensive experiments validate the effectiveness of our approach in enhancing system efficiency and facilitating flexible on-demand privacy-protected data sharing, further solidifying BCFL’s potential in decentralized data sharing scenarios.

中文翻译：

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去中心化物联网数据共享：基于区块链的联邦学习方法，联合优化效率和隐私


基于区块链的联邦学习（BCFL）作为一种有前途的具有隐私保护的去中心化数据共享技术而受到广泛关注。大多数现有的 BCFL 框架松散地耦合了区块链和联邦学习 (FL)。 FL将数据共享转化为模型共享，而区块链则将模型聚合去中心化并处理安全验证。然而，这两种技术的简单叠加通常涉及第三方区块链对等体，导致工作流程效率低下，并可能受到恶意区块链对等体的隐私攻击。一些研究提出了通过添加噪声的差分隐私来防止隐私攻击，但大多数研究不允许客户定制隐私预算，影响了数据可用性并限制了BCFL在数据共享方面的应用。为了应对这些挑战，我们首先引入了一种新颖的紧耦合 BCFL 框架，该框架在客户端集成了训练和挖掘。在此框架下，建立了完全去中心化的数据共享流程。此外，还设计了个性化差分隐私（PDP）机制，使客户能够根据自定义隐私预算将拉普拉斯噪声添加到模型梯度中。为了实现最优的隐私预算，提出了一种基于Stackelberg博弈的隐私优化机制。它为数据请求者和提供者建立效用函数，将效用优化过程建模为Stackelberg博弈过程，在最大化所有参与者效用的同时获得最优隐私预算。这有利于灵活且按需的隐私保护数据共享。 大量的实验验证了我们的方法在提高系统效率和促进灵活的按需隐私保护数据共享方面的有效性，进一步巩固了 BCFL 在去中心化数据共享场景中的潜力。