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

• URL: http://arxiv.org/abs/2409.19756v1
• Date: Sun, 29 Sep 2024 20:02:40 GMT
• Title: Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
• Authors: Ravi Madduri, Zilinghan Li, Tarak Nandi, Kibaek Kim, Minseok Ryu, Alex Rodriguez,
• Abstract summary: 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. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges. 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.
• Score: 0.2748450182087935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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