11 2022 档案
摘要：ACM SIGSAC conference on computer and communications security. 本文主要对目标模型查询样本得出的预测向量进行了优化，通过添加噪声使目标模型在不改变查询样本的预测标签的情况下对预测向量进行具有目的性的有限改变，最终使攻击者的成员推理攻击分类
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摘要：Li, Li, et al. "A review of applications in federated learning." Computers & Industrial Engineering 149 (2020): 106854.CCF-A(AAAI'20） 本论文提出了一种联邦学习框架，可
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摘要：Wang Q, Zhan L, Thompson P, et al. Multimodal learning with incomplete modalities by knowledge distillation[C]//Proceedings of the 26th ACM SIGKDD Int
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摘要：M.A.P.Chamikara, P.Bertok, I.Khalil, D.Liu, S.Camtepe. Privacy Preserving Distributed Machine Learning with Federated Learning. Computer Communication
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摘要：Xiaojin Zhang, Hanlin Gu, Lixin Fan, Kai Chen, and Qiang Yang. 2022. No free lunch theorem for security and utility in federated learning. 1, 1 (Septe
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摘要：Sun X, Liu Y, Li J, et al. Feature evaluation and selection with cooperative game theory[J]. Pattern recognition, 2012, 45(8): 2992-3002. 这篇论文针对在机器学习训
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摘要：Xu, Guowen, et al. "Verifynet: Secure and verifiable federated learning." IEEE Transactions on Information Forensics and Security 15 (2019): 911-926.
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摘要：International Joint Conferences on Artificial Intelligence 微众银行AI团队和中山大学合作发表的论文《FedCG：利用条件生成对抗网络在联邦学习中保护隐私并保持模型性能》提出了"FedCG"，将条件生成对抗网络（cGAN）与分割学习相结合，实
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摘要：Jiayi Chen and Aidong Zhang. 2022. FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks. In Proceedings of t
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摘要：L. Lyu et al., "Towards Fair and Privacy-Preserving Federated Deep Models," in IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 11,
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摘要：Jiawen Kang,Zehui Xiong, Dusit Niyato, Shengli Xie, Junshan Zhang. Incentive Mechanism for Reliable Federated Learning A Joint Optimization Approach t
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摘要：Mahloujifar, Saeed, Esha Ghosh, and Melissa Chase. "Property Inference from Poisoning." 2022 IEEE Symposium on Security and Privacy (SP). IEEE Compute
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摘要：Cheng, Jiangnan, Ao Tang, and Sandeep Chinchali. "Task-aware privacy preservation for multi-dimensional data." International Conference on Machine Lea
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