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Vertical federated learning (vFL) has gained much attention and been deployed to solve machine learning problems with data privacy concerns in recent years.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Measuring and testing dependence by correlation of distances
Gábor J. Székely, Maria L. Rizzo, and Nail K. Bakirov · 2007
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Earlier work this paper cites.
A statistically and numerically efficient independence test based on random projections and distance covariance
Cheng Huang and Xiaoming Huo · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
A formal foundation for secure remote execution of enclaves
Pramod Subramanyan, Rohit Sinha, Ilia Lebedev, Srinivas Devadas, and Sanjit A Seshia · 2017
Earlier work this paper cites.
Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
Cited alongside, same era.
No peek: A survey of private distributed deep learning
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, and Abhimanyu Dubey · 2018
Cited alongside, same era.
Reducing leakage in distributed deep learning for sensitive health data
Praneeth Vepakomma, Otkrist Gupta, Abhimanyu Dubey, and Ramesh Raskar · 2019
Later among the works it cites.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Later among the works it cites.
Asymmetrical vertical federated learning
Yang Liu, Xiong Zhang, and Libin Wang · 2020
Later among the works it cites.
Deep learning with label differential privacy, 2021
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, and Chiyuan Zhang · 2021
Later among the works it cites.
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A fast algorithm for computing distance correlation
Arin Chaudhuri and Wenhao Hu · 2019
Cited alongside, same era.
Distributed differential privacy via shuffling
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
Cited alongside, same era.
Jiankai Sun, Yuanshun Yao, Weihao Gao, Junyuan Xie, and Chong Wang · 2021
Later among the works it cites.
Label leakage and protection in two-party split learning
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, and Chong Wang · 2022
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