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Federated learning in radiology reports poses significant privacy risks, study finds

By PulseAugur Editorial · [2 sources] ·

A new study published on arXiv evaluates the privacy risks associated with federated learning (FL) in the context of radiology reports. Researchers found that sensitive information from these reports can be reconstructed from model updates, even with larger batch sizes and domain-specific tokenizers like GPT-2, RadBERT, and LLaMA-2. The study indicates that tokenizer design significantly impacts the severity of data leakage, suggesting that safeguards such as secure aggregation and differential privacy are crucial for meeting regulatory requirements like HIPAA and GDPR in radiology NLP. AI

IMPACT Highlights the need for enhanced privacy measures in AI models used for clinical text analysis to comply with regulations.

RANK_REASON The cluster contains a research paper published on arXiv detailing a comparative evaluation of privacy risks in federated learning for radiology reports.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Federated learning in radiology reports poses significant privacy risks, study finds

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The cluster contains a research paper published on arXiv detailing a comparative evaluation of privacy risks in federated learning for radiology reports.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Santhosh Parampottupadam, Andres Martinez, Dimitrios Bounias, Sinem Sav, Klaus Maier-Hein, Ralf Floca ·

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

    arXiv:2607.14205v1 Announce Type: cross Abstract: Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information from shared model updates. The extent of this leakage for radiolo…

  2. arXiv cs.CL TIER_1 English(EN) · Ralf Floca ·

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

    Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information from shared model updates. The extent of this leakage for radiology reports, and the role of tokenizer design, rema…

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