Scholarlink

Federated learning (FL) plays a vital role in boosting both accuracy and privacy in the collaborative medical imaging field. The importance of privacy increases with the diverse security standards across nations and corporations, particularly in healthcare and global FL initiatives. Current research on privacy attacks in federated medical imaging focuses on sophisticated gradient inversion attacks that can reconstruct images from FL communications. These methods demonstrate potential worst-case scenarios, highlighting the need for effective security measures and the adoption of comprehensive zero-trust security frameworks. Our paper introduces a novel method for performing precise reconstruction attacks on the private data of participating clients in FL settings using a malicious server. We conducted experiments on brain tumor MRI and chest CT data sets, implementing existing 2D and novel 3D reconstruction techniques. Our results reveal significant privacy breaches: 35.19% of data reconstructed with 6 clients, 37.21% with 12 clients in 2D, and 62.92% in 3D with 12 clients. This underscores the urgent need for enhanced privacy protections in FL systems. To address these issues, we suggest effective measures to counteract such vulnerabilities by securing gradient, analytic, and linear layers. Our contributions aim to strengthen the security framework of FL in medical imaging, promoting the safe advancement of collaborative healthcare research. The source code is available at: https://github.com/MIC-DKFZ/2D3D-Privacy-Attacks-In-federated-Medical-Imaging .

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Client Security Alone Fails in Federated Learning: 2D and 3D Attack Insights

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Santhosh Parampottupadam,
  • Ralf Floca,
  • Dimitrios Bounias,
  • Benjamin Hamm,
  • Saikat Roy,
  • Sinem Sav,
  • Maximilian Zenk,
  • Klaus Maier-Hein

摘要

Federated learning (FL) plays a vital role in boosting both accuracy and privacy in the collaborative medical imaging field. The importance of privacy increases with the diverse security standards across nations and corporations, particularly in healthcare and global FL initiatives. Current research on privacy attacks in federated medical imaging focuses on sophisticated gradient inversion attacks that can reconstruct images from FL communications. These methods demonstrate potential worst-case scenarios, highlighting the need for effective security measures and the adoption of comprehensive zero-trust security frameworks. Our paper introduces a novel method for performing precise reconstruction attacks on the private data of participating clients in FL settings using a malicious server. We conducted experiments on brain tumor MRI and chest CT data sets, implementing existing 2D and novel 3D reconstruction techniques. Our results reveal significant privacy breaches: 35.19% of data reconstructed with 6 clients, 37.21% with 12 clients in 2D, and 62.92% in 3D with 12 clients. This underscores the urgent need for enhanced privacy protections in FL systems. To address these issues, we suggest effective measures to counteract such vulnerabilities by securing gradient, analytic, and linear layers. Our contributions aim to strengthen the security framework of FL in medical imaging, promoting the safe advancement of collaborative healthcare research. The source code is available at: https://github.com/MIC-DKFZ/2D3D-Privacy-Attacks-In-federated-Medical-Imaging .