Authors: Xingyu Li, Lu Peng, Yuping Wang, Weihua Zhang

Published on: May 10, 2024

Impact Score: 8.2

Arxiv code: Arxiv:2405.06784

Summary

• What is new: The integration of foundation models with federated learning for biomedical applications.
• Why this is important: Need to advance biomedical research while ensuring data privacy and security.
• What the research proposes: Using foundation models in combination with federated learning to process and analyze medical data without compromising privacy.
• Results: Enhanced capabilities in medical diagnostics and personalized treatment, with improved data privacy.

Technical Details

Technological frameworks used: Federated learning

Models used: ChatGPT, LLaMa, CLIP

Data used: Clinical reports, diagnostic images, multimodal patient data

Potential Impact

Healthcare providers, medical diagnostics companies, health data security firms

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