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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)

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## 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)
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