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Despite recent progress in enhancing the privacy of federated learning (FL)via differential privacy (DP), the trade-off of DP between privacy protectionand performance is still underexplored for real-world medical scenario. In thispaper, we propose to optimize the trade-off under the context of client-levelDP, which focuses on privacy during communications. However, FL for medicalimaging involves typically much fewer participants (hospitals) than otherdomains (e.g., mobile devices), thus ensuring clients be differentially privateis much more challenging. To tackle this problem, we propose an adaptiveintermediary strategy to improve performance without harming privacy.Specifically, we theoretically find splitting clients into sub-clients, whichserve as intermediaries between hospitals and the server, can mitigate thenoises introduced by DP without harming privacy. Our proposed approach isempirically evaluated on both classification and segmentation tasks using twopublic datasets, and its effectiveness is demonstrated with significantperformance improvements and comprehensive analytical studies. Code isavailable at: https://github.com/med-air/Client-DP-FL.

Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging