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

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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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/tps-isa62245.2024.00039
Citations
4
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
1.39

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article

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

Ravi Madduri, Kibaek Kim, Zilinghan Li, Tarak Nandi et al.
4 citations
Privacy-Preserving Technologies in Data
1.39
article

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

Ravi Madduri, Kibaek Kim, Zilinghan Li, Tarak Nandi, Minseok Ryu, Alex C. Rodriguez
article en
4 citations

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.

Argonne National Laboratory (US), Arizona State University (US)
U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research
Openalex Percentile: Top 15%
Privacy-Preserving Technologies in Data
1.39
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Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System — Ravi Madduri, Kibaek Kim, et al. · (2024) | TGRS Research Map | TGRS