Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System

TL;DR

This paper discusses how Privacy-Preserving Federated Learning can enable a self-improving healthcare system by allowing collaborative analysis of decentralized patient data while maintaining privacy.

Contribution

It proposes a vision for integrating PPFL into healthcare to realize a truly learning healthcare system as defined by IOM.

Findings

PPFL can address data sharing and privacy challenges in healthcare.

Integration of PPFL can enable continuous, collaborative healthcare data analysis.

A framework for implementing PPFL in healthcare systems is outlined.

Abstract

The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable.