WebQuorumChain: A web framework for quorum-based health care model learning.

Background: 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.

Method: 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.

Results: 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.

Conclusions: 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.