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：尽管近年来通过差分隐私（DP）提升联邦学习（FL）隐私保护方面取得了进展，但在真实医学场景中，DP在隐私保护与性能之间的权衡仍未被充分探索。本文提出在客户端级差分隐私（关注通信过程中的隐私保护）背景下优化这一权衡。然而，与其他领域（如移动设备）相比，医学影像的联邦学习通常参与者（医院）数量更少，因此确保客户端具备差分隐私更具挑战性。为解决此问题，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）可以在不损害隐私的情况下缓解DP引入的噪声。我们的方法在两个公开数据集上针对分类和分割任务进行了实证评估，其有效性通过显著的性能改进和全面的分析研究得到验证。代码地址：https://github.com/med-air/Client-DP-FL。