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

2024/09/29 by Ravi Madduri, Madduri, Ravi, Zilinghan Li +9 · 1 citation
Computer Science · #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Parallel #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2409.19756

openalex publication_date 2024/09/29 · openalex created_date 2024/10/28 · openalex updated_date 2026/07/30

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.

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