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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.

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Abstract: Client Security Alone Fails in Federated Learning

  • 会议论文

  • 发表日期：

    2025年

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  • 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.