摘要：
"Nasr M, Songi S, Thakurta A, et al. Adversary instantiation: Lower bounds for differentially private machine learning[C]//2021 IEEE Symposium on secu 阅读全文
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Mugunthan, V. , A. Peraire-Bueno , and L. Kagal . "PrivacyFL: A simulator for privacy-preserving and secure federated learning.", 10.1145/3340531.3412 阅读全文
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Thapa, C. , M. Chamikara , and S. Camtepe . "SplitFed: When Federated Learning Meets Split Learning." (2020). 本文提出了一种联邦学习（FL）和分割学习（SL）的混合方法（SFL），能够同时解 阅读全文
摘要：
"Jayaraman B, Evans D. Evaluating differentially private machine learning in practice[C]//USENIX Security Symposium. 2019." 本文对机器学习不同隐私机制进行评估。评估重点放在梯度 阅读全文
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Rothchild, Daniel, et al. "Fetchsgd: Communication-efficient federated learning with sketching." International Conference on Machine Learning. PMLR, 2 阅读全文