Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx

2024/12/13 by Trung-Hieu Hoang, Jordan Fuhrman, Marcus D. R. Klarqvist +9 · 1 voice
Computer Science · Medicine · #Machine Learning in Healthcare #Privacy-Preserving Technologies in Data #Radiomics and Machine Learning in Medical Imaging

paper · doi:10.1016/j.csbj.2024.12.001

openalex publication_date 2024/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

. Furthermore, it is completely agnostic to the underlying computational infrastructure of participating clients, allowing an instantaneous deployment of this framework into existing computing infrastructures. Experimentally, the utility of APPFLx is demonstrated in two case studies: (1) predicting participant age from electrocardiogram (ECG) waveforms, and (2) detecting COVID-19 disease from chest radiographs. Here, ML models were securely trained across heterogeneous computing resources, including a combination of on-premise high-performance computing and cloud computing facilities. By securely unlocking data from multiple sources for training without directly sharing it, these FL models enhance generalizability and performance compared to centralized training models while ensuring data remains protected. In conclusion, APPFLx demonstrated itself as an easy-to-use framework for accelerating biomedical studies across organizations and healthcare systems on large datasets while maintaining the protection of private medical data.

Citations

• Federated Learning With Differential Privacy: Algorithms and Performance Analysis

Discussions

• 🔐This study introduces APPFLx, a secure and scalable Federated Learning (FL) framework designed to accelerate privacy-preserving biomedical research across institutions with heterogeneous computing e [bsky, 1 points, 0 comments]

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