The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL participant. Furthermore, there is no global model verifiability available in the current architecture. This paper proposes a smart contract-based policy control for securing the Federated Learning (FL) management system. First, we develop and deploy a smart contract-based local training policy control on the FL participants' side. This policy control is used to verify the training process, ensuring that the evaluation process follows the same rules for all FL participants. We then enforce a smart contract-based aggregation policy to manage the global model aggregation process. Upon completion, the aggregated model and policy are stored on blockchain-based storage. Subsequently, we distribute the aggregated global model and the smart contract to all FL participants. Our proposed method uses smart policy control to manage access and verify the integrity of machine learning models. We conducted multiple experiments with various machine learning architectures and datasets to evaluate our proposed framework, such as MNIST and CIFAR-10.

翻译：随着物联网设备在智慧城市、智能医疗系统及各类实际应用中的广泛部署，海量数据随之产生，这些数据通常借助不同的机器学习模型进行分析。联邦学习作为一种隐私保护型机器学习技术，允许多方在不交换原始数据的情况下协同训练机器学习模型，因而得到广泛认可。然而，现有联邦学习架构因各参与方实施不同的数据保护策略，无法对训练过程进行审计。此外，当前架构也不具备全局模型可验证性。本文提出一种基于智能合约的策略控制方法，用于保障联邦学习管理系统的安全。首先，我们在联邦学习参与方侧开发并部署基于智能合约的本地训练策略控制，该策略控制用于验证训练过程，确保所有参与方的评估流程遵循统一规则。接着，我们实施基于智能合约的聚合策略，以管理全局模型的聚合流程。聚合完成后，聚合模型及策略将存储于基于区块链的存储系统中。随后，我们将聚合后的全局模型及智能合约分发给所有联邦学习参与方。所提方法利用智能策略控制来管理访问并验证机器学习模型的完整性。我们基于多种机器学习架构与数据集（如MNIST和CIFAR-10）开展了多项实验，以评估所提框架的性能。