Despite recent progress in enhancing the privacy of federated learning (FL) via differential privacy (DP), the trade-off of DP between privacy protection and performance is still underexplored for real-world medical scenario. In this paper, we propose to optimize the trade-off under the context of client-level DP, which focuses on privacy during communications. However, FL for medical imaging involves typically much fewer participants (hospitals) than other domains (e.g., mobile devices), thus ensuring clients be differentially private is much more challenging. To tackle this problem, we propose an adaptive intermediary strategy to improve performance without harming privacy. Specifically, we theoretically find splitting clients into sub-clients, which serve as intermediaries between hospitals and the server, can mitigate the noises introduced by DP without harming privacy. Our proposed approach is empirically evaluated on both classification and segmentation tasks using two public datasets, and its effectiveness is demonstrated with significant performance improvements and comprehensive analytical studies. Code is available at: https://github.com/med-air/Client-DP-FL.

翻译：尽管近期差分隐私技术在增强联邦学习隐私保护方面取得了进展，但差分隐私在隐私保护与性能之间的权衡在真实医学场景中仍未得到充分探索。本文提出在客户端级差分隐私背景下优化这一权衡，该领域主要关注通信过程中的隐私问题。然而，面向医学影像的联邦学习参与方（医院）通常远少于其他领域（如移动设备），因此确保客户端的差分隐私更具挑战性。为解决该问题，我们提出一种自适应中介策略，在不损害隐私的前提下提升性能。具体而言，我们从理论上发现，将客户端拆分为子客户端作为医院与服务器之间的中介，可缓解差分隐私引入的噪声而不损害隐私。我们在两个公开数据集上对分类和分割任务进行了实证评估，通过显著的性能提升和全面的分析研究验证了该方法的有效性。代码地址：https://github.com/med-air/Client-DP-FL。