This paper proposes a blockchain-based Federated Learning (FL) framework with Intel Software Guard Extension (SGX)-based Trusted Execution Environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.

翻译：本文提出了一种基于区块链的联邦学习框架，该框架采用英特尔软件保护扩展（SGX）驱动的可信执行环境（TEE），用于在工业物联网（IIoT）环境中安全聚合本地模型。在联邦学习中，本地模型可能遭受攻击者篡改，从而导致由被篡改本地模型生成的全局模型出现错误。为此，所提框架利用区块链网络实现安全模型聚合：每个区块链节点均搭载支持SGX的处理器，可在安全环境下执行基于联邦学习的聚合任务以生成全局模型。区块链节点能够验证聚合模型的真实性，通过运行区块链共识机制保障模型完整性，并将模型存入分布式账本实现防篡改存储。每个集群在使用聚合模型前，可从区块链获取模型并验证其完整性。我们采用不同CNN模型与数据集开展了多项实验，以评估所提框架的性能。