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

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

TL;DR

The paper addresses the challenge of realizing a truly learning healthcare system while protecting patient privacy. It advocates privacy-preserving federated learning (PPFL) and documents the APPFL/APPFLx framework, including differential privacy and secure aggregation. It extends PPFL to multimodal biomedical data through architectures like HierFL and federated continuous learning, with evaluation and data readiness via AIDRIN. It also tackles practical deployment via cost-aware cloud scheduling to enable scalable adoption across resource-constrained institutions.

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.

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

TL;DR

The paper addresses the challenge of realizing a truly learning healthcare system while protecting patient privacy. It advocates privacy-preserving federated learning (PPFL) and documents the APPFL/APPFLx framework, including differential privacy and secure aggregation. It extends PPFL to multimodal biomedical data through architectures like HierFL and federated continuous learning, with evaluation and data readiness via AIDRIN. It also tackles practical deployment via cost-aware cloud scheduling to enable scalable adoption across resource-constrained institutions.

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.
Paper Structure (9 sections, 5 figures)

This paper contains 9 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Privacy Preserving Federated Learning
  3. Federated Training of Multimodal Biomedical Models
  4. Multimodal Learning
  5. Integration of Multimodal Learning in a Federated Setting
  6. Hierarchical FL for Collaborative Training Beyond the Trust Boundaries
  7. Federated Continuous Learning for Multimodal Biomedical Models
  8. Cost-Aware FL on the Cloud
  9. Conclusions

Figures (5)

  • Figure 1: An envisioned privacy-preserving federated learning framework for a truly learning healthcare system: Under the coordination of a trusted and secure server, multiple hospitals collaboratively train robust, generalized machine learning models using multimodal biomedical data stored in their cloud or on-premise facilities. With continuous learning capabilities integrated into the framework, the models can detect and avoid performance degradation, adapt dynamically in real time to any shifts in data distributions, availability of new patient data, and evolving health trends.
  • Figure 2: Different sample and multimodal feature distribution patterns among clients in multimodal federated learning.
  • Figure 3: Hierarchical federated learning helps to connect several small trust group to a larger federation.
  • Figure 4: Federated continuous learning workflow with a federated evaluation feedback loop for timely performance degradation detection.
  • Figure 5: Using a server-side cost-aware scheduler to achieve cost-effective FL experiments among clients on the cloud.