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.
- federated learning
- General Data Protection Regulation
- GPT-2
- Health Insurance Portability and Accountability Act
- LLaMA-2
- MIMIC-CXR
- Radiology reports
- Santhosh Parampottupadam
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