From ae77fc802f0648525f0059af1a50f77c6ec38fd5 Mon Sep 17 00:00:00 2001
From: Yuecheng Li <93138940+6lyc@users.noreply.github.com>
Date: Sun, 31 Aug 2025 19:18:17 +0800
Subject: [PATCH] Update README.md

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 README.md | 2 ++
 1 file changed, 2 insertions(+)

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 ### 5.2 Data Privacy and Confidentiality
 
+* [Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off](https://arxiv.org/abs/2402.07002) [[Code](https://github.com/6lyc/FedCEO_Collaborate-with-Each-Other)] [SYSU, TAMU, HITSZ, ZSTU] (ICML 2025)
 * [Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning](https://arxiv.org/abs/1805.05838) （NIPS 2019 Workshop)
 * [Quantification of the Leakage in Federated Learning](https://arxiv.org/pdf/1910.05467.pdf)
 
@@ -502,6 +503,7 @@
 
 ## Part 13: Secure Multi-party Computation(MPC)
 ### 13.1 Differential Privacy
+* [Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off](https://arxiv.org/abs/2402.07002) [[Code](https://github.com/6lyc/FedCEO_Collaborate-with-Each-Other)] [SYSU, TAMU, HITSZ, ZSTU] (ICML 2025)
 * [Learning Differentially Private Recurrent Language Models](https://arxiv.org/abs/1710.06963)
 * [Federated Learning with Bayesian Differential Privacy](https://arxiv.org/abs/1911.10071) （NIPS 2019 Workshop)
 * [Private Federated Learning with Domain Adaptation](https://arxiv.org/abs/1912.06733) （NIPS 2019 Workshop)