federated learning 2compliance 1differential privacy 1gradient inversion 1healthcare 1privacy leakage 1privacy-preserving AI 1radiology reports 1tokenizer design 1
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cs.LG2026
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
Santhosh Parampottupadam, Andres Martinez, Dimitrios Bounias +3
The paper measures how much patient information can be reconstructed from model gradients in federated learning of radiology reports, comparing three different tokenizers and showi…
cs.LG2026
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
Santhosh Parampottupadam, Melih CoÅÄun, Sarthak Pati +7
The paper proposes a federated learning framework that adjusts differential privacy noise based on each healthcare institution's compliance level, allowing lower‑compliance sites t…