Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4925-4928. doi: 10.1109/EMBC48229.2022.9871371.
Cerebellar ataxia (CA) refers to the incoordination of movements of the eyes, speech, trunk, and limbs caused by cerebellar dysfunction. Conventional machine learning (ML) utilizes centralised databases to train a model of diagnosing CA. Despite the high accuracy, these approaches raise privacy concern as participants' data revealed in the data centre. Federated learning is an effective distributed solution to exchange only the ML model weight rather than the raw data. However, FL is also vulnerable to network attacks from malicious devices. In this study, we depict the concept of blockchained FL with individual's validators. We simulate the proposed approach with real-world dataset collected from kinematic sensors of CA individuals with four geographically separated clinics. Experimental results show the blockchained FL maintains competitive accuracy of 89.30%, while preserving both privacy and security.
小脑性共济失调（CA）是指由于小脑功能障碍导致的眼球、言语、躯干和四肢运动不协调。传统的机器学习（ML）利用集中式数据库来训练诊断 CA 的模型。尽管这些方法具有很高的准确性，但它们也引发了隐私问题，因为参与者的数据在数据中心中被揭示出来。联邦学习是一种有效的分布式解决方案，它只交换机器学习模型的权重，而不是原始数据。然而，联邦学习也容易受到来自恶意设备的网络攻击。在这项研究中，我们描述了具有个体验证者的区块链联邦学习的概念。我们使用从四个地理位置分离的诊所的 CA 个体的运动传感器收集的真实世界数据集来模拟所提出的方法。实验结果表明，区块链联邦学习保持了 89.30%的竞争准确性，同时保护了隐私和安全。
