arXiv AI July 15, 2026 By Santhosh Parampottupadam, Melih Co\c{s}\u{g}un, Sarthak Pati, Maximilian Zenk, Saikat Roy, Dimitrios Bounias, Benjamin Hamm, Sinem Sav, Ralf Floca, Klaus Maier-Hein

Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI

Read the original on arXiv AI →

arXiv:2505. 22108v4 Announce Type: replace-cross Abstract: Background: Federated learning (FL) enables collaborative training of clinical AI models without centralizing patient data, but adoption is limited by privacy concerns, heterogeneous institutional compliance, and resource disparities; standard differential privacy (DP) applies uniform noise to all clients, penalizing well-compliant or under-resourced institutions.

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computer-vision

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