Author

Santhosh Parampottupadam

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Open access Oct 2026

Efficient Privacy-Preserving Medical Cross-Silo Federated Learning.

Federated Learning (FL) is becoming increasingly important for advancing machine learning in the medical domain, as it enables collaborative model training across multiple institutions without requiring them to share raw patient data. Unlike traditional centralized machine learning, which gathers data and trains the mo...

Benjamin Hamm, Yannick Kirchhoff, Maximilian R. Rokuss et al. · IEEE journal of biomedical a... · 2 citations

Jul 2026

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks

Substantial portions of report text are recoverable from FL gradients even at larger batch sizes and with domain-specific tokenizers, and safeguards such as secure aggregation and differential privacy are likely necessary to meet HIPAA and GDPR requirements for FL in radiology NLP.

Santhosh Parampottupadam, Andres Martinez, D. Bounias et al. · arXiv.org · 0 citations