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

Year: 2024

Type: conference-paper

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.

Source: IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

DOI: 10.1109/tps-isa62245.2024.00039

Authors: Ravi Madduri, Zilinghan Li, Tarak Nandi, Kibaek Kim, Minseok Ryu, Alex Rodriguez

Institutions: Argonne National Laboratory, Arizona State University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research