Due to the rising awareness of privacy and security in machine learning applications, federated learning (FL) has received widespread attention and applied to several areas, e.g., intelligence healthcare systems, IoT-based industries, and smart cities. FL enables clients to train a global model collaboratively without accessing their local training data. However, the current FL schemes are vulnerable to adversarial attacks. Its architecture makes detecting and defending against malicious model updates difficult. In addition, most recent studies to detect FL from malicious updates while maintaining the model's privacy have not been sufficiently explored. This paper proposed blockchain-based federated learning with SMPC model verification against poisoning attacks for healthcare systems. First, we check the machine learning model from the FL participants through an encrypted inference process and remove the compromised model. Once the participants' local models have been verified, the models are sent to the blockchain node to be securely aggregated. We conducted several experiments with different medical datasets to evaluate our proposed framework.

翻译：随着机器学习应用中对隐私与安全意识的提升，联邦学习（FL）已受到广泛关注，并被应用于多个领域，例如智能医疗系统、物联网产业和智慧城市。联邦学习使客户端无需访问其本地训练数据即可协作训练全局模型。然而，当前的联邦学习方案容易受到对抗性攻击。其架构使得检测和防御恶意模型更新变得困难。此外，近年来关于在保持模型隐私的同时检测联邦学习中恶意更新的研究尚未得到充分探索。本文针对医疗系统提出了一种基于区块链的联邦学习，结合安全多方计算（SMPC）模型验证以抵御投毒攻击。首先，我们通过加密推理过程检查来自联邦学习参与者的机器学习模型，并剔除受损模型。一旦参与者的本地模型通过验证，这些模型将被发送至区块链节点进行安全聚合。我们使用多种医学数据集进行了多项实验，以评估所提出框架的性能。