Federated Learning for Healthcare Informatics

TL;DR

Federated learning enables collaborative healthcare data analysis by training shared models across institutions without sharing sensitive data, addressing privacy and fragmentation challenges in biomedical informatics.

Contribution

This survey reviews federated learning methods tailored for healthcare, highlighting solutions to statistical, system, and privacy challenges in biomedical applications.

Findings

Federated learning preserves patient privacy while enabling collaborative model training.

It addresses data fragmentation issues across healthcare institutions.

Potential to improve healthcare analytics and decision-making.

Abstract

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data". Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges and privacy issues in federated learning, and point out the implications and potentials in healthcare.