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Fed-BioMed: A general open-source frontendframework for federated learning in healthcare

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Abstract

While data in healthcare is produced in quantities never imagined before, the feasibility of clinical studies is often hindered by the problem of data access and transfer, especially regarding privacy concerns. Federated learning allows privacy-preserving data analyses using decentralized optimization approaches keeping data securely decentralized. There are currently initiatives providing federated learning frameworks , which are however tailored to specific hardware and modeling approaches, and do not provide natively a deployable production-ready environment. To tackle this issue, herein we propose an open-source fed-erated learning frontend framework with application in healthcare. Our framework is based on a general architecture accommodating for different models and optimization methods. We present software components for clients and central node, and we illustrate the workflow for deploying learning models. We finally provide a real-world application to the federated analysis of multi-centric brain imaging data.
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Dates and versions

hal-02966789 , version 1 (14-10-2020)

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  • HAL Id : hal-02966789 , version 1

Cite

Santiago Silva, Andre Altmann, Boris Gutman, Marco Lorenzi. Fed-BioMed: A general open-source frontendframework for federated learning in healthcare. MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention - 1st Workshop on Distributed and Collaborative Learning, Oct 2020, Lima/ Virtuel, Peru. pp.201-210. ⟨hal-02966789⟩
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