Detecting model misconducts in decentralized healthcare federated learning.

ABSTRACT:

Background

To accelerate healthcare/genomic medicine research and facilitate quality improvement, researchers have started cross-institutional collaborations to use artificial intelligence on clinical/genomic data. However, there are real-world risks of incorrect models being submitted to the learning process, due to either unforeseen accidents or malicious intent. This may reduce the incentives for institutions to participate in the federated modeling consortium. Existing methods to deal with this "model misconduct" issue mainly focus on modifying the learning methods, and therefore are more specifically tied with the algorithm.

Basic procedures

In this paper, we aim at solving the problem in an algorithm-agnostic way by (1) designing a simulator to generate various types o

SUBMITTER: Kuo TT 

PROVIDER: S-EPMC10017272 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature