Inclusive, Differentially Private Federated Learning for Clinical Data

• URL: http://arxiv.org/abs/2505.22108v2
• Date: Thu, 05 Jun 2025 09:01:10 GMT
• Title: Inclusive, Differentially Private Federated Learning for Clinical Data
• Authors: Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati, Maximilian Zenk, Saikat Roy, Dimitrios Bounias, Benjamin Hamm, Sinem Sav, Ralf Floca, Klaus Maier-Hein,
• Abstract summary: Federated Learning (FL) offers a promising approach for training clinical AI models without centralizing sensitive patient data.<n>Its real-world adoption is hindered by challenges related to privacy, resource constraints, and compliance.<n>We propose a novel FL framework that enhances DP by adaptively adjusting noise based on quantifiable client compliance scores.
• Score: 0.45034038207097465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) offers a promising approach for training clinical AI models without centralizing sensitive patient data. However, its real-world adoption is hindered by challenges related to privacy, resource constraints, and compliance. Existing Differential Privacy (DP) approaches often apply uniform noise, which disproportionately degrades model performance, even among well-compliant institutions. In this work, we propose a novel compliance-aware FL framework that enhances DP by adaptively adjusting noise based on quantifiable client compliance scores. Additionally, we introduce a compliance scoring tool based on key healthcare and security standards to promote secure, inclusive, and equitable participation across diverse clinical settings. Extensive experiments on public datasets demonstrate that integrating under-resourced, less compliant clinics with highly regulated institutions yields accuracy improvements of up to 15% over traditional FL. This work advances FL by balancing privacy, compliance, and performance, making it a viable solution for real-world clinical workflows in global healthcare.

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