Shao Xiyan, Pham Anh, Kuo Tsung-Ting
UCSD Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Inform Med Unlocked. 2024;50. doi: 10.1016/j.imu.2024.101590. Epub 2024 Oct 11.
Institutions interested in collaborative machine learning to enhance healthcare may be deterred by privacy concerns. Decentralized federated learning is a privacy-preserving and security-robust tool to promote cross-institutional learning, however, such frameworks require complex setups and advanced technical expertise. Here, we aim to improve their utilization by offering an intuitive, user-friendly, and secure system that integrates both front-end and back-end functionalities.
We develop WebQuorumChain, an integrated system built upon the QuorumChain schema. We test the system on a 2-site network using two publicly available health datasets and measure the average vertical and horizontal-ensemble AUCs per dataset across 30 trials, as well as the average execution time of the system.
Our system achieved consistently high AUCs for each dataset (0.94-0.96), with reasonable total execution times ranging from 5 to 20 min, inclusive of modeling and all other system overheads. The front-end displays event logs generated from back-end layers in real time, in sync with the progress of the underlying algorithm.
We develop a web-based system that supplies users with visual tools to configure the federated learning network, manage training sessions, and inspect the learning process. WebQuorumChain helps schedule and monitor low-level processes without violating the fundamental security promises of cross-institutional decentralized machine learning. The system also maintains predictive accuracy and runtime efficiency in the presence of additional layers. WebQuorumChain will help promote meaningful collaboration among healthcare institutions, who can retain full control of their data privacy while contributing to data-driven discoveries.
对通过协作式机器学习来改善医疗保健感兴趣的机构可能会因隐私问题而望而却步。去中心化联邦学习是一种保护隐私且安全稳健的工具，可促进跨机构学习，然而，此类框架需要复杂的设置和先进的技术专长。在此，我们旨在通过提供一个集成了前端和后端功能的直观、用户友好且安全的系统来提高它们的利用率。
我们开发了WebQuorumChain，这是一个基于QuorumChain架构构建的集成系统。我们使用两个公开可用的健康数据集在一个双站点网络上测试该系统，并在30次试验中测量每个数据集的平均垂直和水平集成AUC，以及系统的平均执行时间。
我们的系统在每个数据集上均实现了持续较高的AUC（0.94 - 0.96），总执行时间合理，范围为5至20分钟，包括建模和所有其他系统开销。前端实时显示从后端层生成的事件日志，与底层算法的进度同步。
我们开发了一个基于网络的系统，为用户提供可视化工具来配置联邦学习网络、管理训练会话并检查学习过程。WebQuorumChain有助于调度和监控底层流程，同时不违反跨机构去中心化机器学习的基本安全承诺。该系统在存在额外层的情况下也能保持预测准确性和运行时效率。WebQuorumChain将有助于促进医疗机构之间有意义的合作，这些机构在为数据驱动的发现做出贡献的同时，可以完全控制其数据隐私。
