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Split learning is a distributed training framework that allows multiple parties to jointly train a machine learning model over vertically partitioned data (partitioned by attributes).
A quasi-newton method based vertical federated learning framework for logistic regression
Kai Yang, Tao Fan, Tianjian Chen, Yuanming Shi, and Qiang Yang · 1912
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Differentially private m-estimators
Jing Lei · 2011
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Differentially private naive bayes classification
Jaideep Vaidya, Basit Shafiq, Anirban Basu, and Yuan Hong · 2013
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https://www.kaggle.com/c/criteo-display-ad-challenge/data , 2014
Display advertising challenge · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
https://www.kaggle.com/c/avazu-ctr-prediction/data , 2015
Click-through rate prediction · 2015
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
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.
Secure linear regression on vertically partitioned datasets
Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans · 2016
Earlier work this paper cites.
Efficient batched oblivious prf with applications to private set intersection
Vladimir Kolesnikov, Ranjit Kumaresan, Mike Rosulek, and Ni Trieu · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
Cited alongside, same era.
Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
Cited alongside, same era.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Can we use split learning on 1d cnn models for privacy preserving training?
Sharif Abuadbba, Kyuyeon Kim, Minki Kim, Chandra Thapa, Seyit A Camtepe, Yansong Gao, Hyoungshick Kim, and Surya Nepal · 2020
Later among the works it cites.
Splitnn-driven vertical partitioning
Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, and Ramesh Raskar · 2020
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Vafl: a method of vertical asynchronous federated learning
Tianyi Chen, Xiao Jin, Yuejiao Sun, and Wotao Yin · 2020
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An efficient framework for clustered federated learning
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran · 2020
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Lower bounds and optimal algorithms for personalized federated learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtarik · 2020
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Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
The us census bureau adopts differential privacy
John M Abowd · 2018
Cited alongside, same era.
Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
Cited alongside, same era.
Secure logistic regression based on homomorphic encryption: Design and evaluation
Miran Kim, Yongsoo Song, Shuang Wang, Yuhou Xia, Xiaoqian Jiang, et al · 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.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ulfar Erlingsson · 2018
Cited alongside, same era.
Scalable private set intersection based on ot extension
Benny Pinkas, Thomas Schneider, and Michael Zohner · 2018
Cited alongside, same era.
Differentially private regression with gaussian processes
Michael Smith, Mauricio Álvarez, Max Zwiessele, and Neil D Lawrence · 2018
Cited alongside, same era.
Scalable differential privacy with certified robustness in adversarial learning
Hai Phan, My T Thai, Han Hu, Ruoming Jin, Tong Sun, and Dejing Dou · 2020
Later among the works it cites.
Differentially private learning needs better features (or much more data)
Florian Tramer and Dan Boneh · 2020
Later among the works it cites.
Hybrid differentially private federated learning on vertically partitioned data
Chang Wang, Jian Liang, Mingkai Huang, Bing Bai, Kun Bai, and Hao Li · 2020
Later among the works it cites.
Federated accelerated stochastic gradient descent
Honglin Yuan and Tengyu Ma · 2020
Later among the works it cites.
Ege Erdogan, Alptekin Kupcu, and A Ercument Cicek · 2021
Later among the works it cites.
On deep learning with label differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, and Chiyuan Zhang · 2021
Later among the works it cites.
Antipodes of label differential privacy: Pate and alibi
Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, and Florian Tramer · 2021
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Vertical federated learning without revealing intersection membership
Jiankai Sun, Xin Yang, Yuanshun Yao, Aonan Zhang, 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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