Authors: Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu

Published on: September 27, 2024

Impact Score: 8.2

Arxiv code: Arxiv:2409.18907

Summary

• What is new: The paper presents a holistic framework called MedPFL for analyzing and mitigating privacy risks in federated learning for medical data, and empirically demonstrates severe privacy risks along with the limitations of current defense mechanisms.
• Why this is important: Federated learning’s default settings may expose sensitive medical data to privacy attacks, and the extent of these risks and effective mitigation strategies are not well understood.
• What the research proposes: The proposed solution is the MedPFL framework, which provides a comprehensive approach to analyze and mitigate privacy risks in federated learning for medical data.
• Results: The study reveals significant privacy risks in federated learning for medical images and finds that adding random noises, a common defense mechanism, may not be sufficient to protect privacy. Extensive experiments on benchmark datasets highlight these challenges and the need for more effective solutions.

Technical Details

Technological frameworks used: MedPFL

Models used: Federated learning models tailored for medical data

Data used: Several benchmark medical image datasets

Potential Impact

Healthcare providers, medical imaging companies, and AI-driven healthcare solutions could be significantly impacted by these findings, raising the need for more robust privacy protection methods in developing AI systems for medical use.

Want to implement this idea in a business?

We have generated a startup concept here: MedPFL Shield.