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

arXiv: https://arxiv.org/abs/2409.19756 · HTML: https://mlsanity.com/paper/2409.19756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [Differentially private federated learning for localized control of infectious disease dynamics](https://mlsanity.com/paper/2509.14024/) (2025)

## Abstract (arXiv)

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.