Update README.md

fastDP/README.md

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 ### References
 [1] Goodfellow, Ian. "Efficient per-example gradient computations." arXiv preprint arXiv:1510.01799 (2015).
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 [2] Li, Xuechen, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. "Large language models can be strong differentially private learners." arXiv preprint arXiv:2110.05679 (2021).
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 [3] Bu, Zhiqi, Jialin Mao, and Shiyun Xu. "Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy." arXiv preprint arXiv:2205.10683 (2022).
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 [4] Bu, Zhiqi, Yu-Xiang Wang, Sheng Zha, and George Karypis. "Differentially Private Optimization on Large Model at Small Cost." arXiv preprint arXiv:2210.00038 (2022).
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 [5] Yousefpour, Ashkan, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen et al. "Opacus: User-friendly differential privacy library in PyTorch." arXiv preprint arXiv:2109.12298 (2021).
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 [6] Bu, Zhiqi, Yu-Xiang Wang, Sheng Zha, and George Karypis. "Differentially Private Bias-Term only Fine-tuning of Foundation Models." arXiv preprint arXiv:2210.00036 (2022).