1. Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan. 2018. cpSGD: Communication-efficient and differentially-private distributed SGD. Advances in Neural Information Processing Systems 31 (2018).
2. Differentially private learning with adaptive clipping;Andrew Galen;Advances in Neural Information Processing Systems,2021
3. Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, 2017. Privacy-preserving deep learning via additively homomorphic encryption. IEEE transactions on information forensics and security 13, 5 (2017), 1333–1345.
4. Hanxiao Chen, Meng Hao, Hongwei Li, Kangjie Chen, Guowen Xu, Tianwei Zhang, and Xilin Zhang. 2023. GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA(Proceedings of Machine Learning Research, Vol. 202), Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 4566–4584. https://proceedings.mlr.press/v202/chen23j.html
5. SecureBoost: A Lossless Federated Learning Framework