Fetching the paper…
Reading the bibliography…
Distributed machine learning has been widely studied in order to handle exploding amount of data.
Privacy preserving association rule mining in vertically partitioned data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 639–644
Jaideep Vaidya and Chris Clifton. 2002 · 2002
Earlier work this paper cites.
Result analysis of the NIPS 2003 feature selection challenge. In Advances in neural information processing systems . 545–552
Isabelle Guyon, Steve Gunn, Asa Ben-Hur, and Gideon Dror. 2005 · 2003
Earlier work this paper cites.
Privacy-preserving k-means clustering over vertically partitioned data. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 206–215
Jaideep Vaidya and Chris Clifton. 2003 · 2003
Earlier work this paper cites.
Privacy-preserving datamining on vertically partitioned databases. In Annual International Cryptology Conference . Springer, 528–544
Cynthia Dwork and Kobbi Nissim. 2004 · 2004
Earlier work this paper cites.
Privacy preserving mining of association rules
Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, and Johannes Gehrke. 2004 · 2004
Earlier work this paper cites.
Privacy-preservation for gradient descent methods. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 775–783
Li Wan, Wee Keong Ng, Shuguo Han, and Vincent Lee. 2007 · 2007
Earlier work this paper cites.
Differential privacy: A survey of results. In International Conference on Theory and Applications of Models of Computation . Springer, 1–19
Cynthia Dwork. 2008 · 2008
Earlier work this paper cites.
Privacy-preserving logistic regression. In Advances in neural information processing systems . 289–296
Kamalika Chaudhuri and Claire Monteleoni. 2009 · 2009
Earlier work this paper cites.
Multiparty differential privacy via aggregation of locally trained classifiers. In Advances in Neural Information Processing Systems . 1876–1884
Manas Pathak, Shantanu Rane, and Bhiksha Raj. 2010 · 2010
Earlier work this paper cites.
Security and privacy in cloud computing: A survey. In 2010 Sixth International Conference on Semantics, Knowledge and Grids . IEEE, 105–112
Minqi Zhou, Rong Zhang, Wei Xie, Weining Qian, and Aoying Zhou. 2010 · 2010
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al · 2011
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011 · 2011
Earlier work this paper cites.
Privacy via the johnson-lindenstrauss transform
Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra. 2012 · 2012
Earlier work this paper cites.
A differentially private stochastic gradient descent algorithm for multiparty classification. In Artificial Intelligence and Statistics . 933–941
Arun Rajkumar and Shivani Agarwal. 2012 · 2012
Earlier work this paper cites.
More effective distributed ml via a stale synchronous parallel parameter server. In Advances in neural information processing systems . 1223–1231
Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B Gibbons, Garth A Gibson, Greg Ganger, and Eric P Xing. 2013 · 2013
Earlier work this paper cites.
Privacy via the Johnson-Lindenstrauss Transform
Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra. 2013 · 2013
Cited alongside, same era.
Signal processing and machine learning with differential privacy: Algorithms and challenges for continuous data
Anand D Sarwate and Kamalika Chaudhuri. 2013 · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Cited alongside, same era.
On the linear convergence of the ADMM in decentralized consensus optimization
Wei Shi, Qing Ling, Kun Yuan, Gang Wu, and Wotao Yin. 2014 · 2014
Cited alongside, same era.
Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security . ACM, 1310–1321
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
Cited alongside, same era.
Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds.. In NSDI . 629–647
Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R Ganger, Phillip B Gibbons, and Onur Mutlu. 2017 · 2017
Later among the works it cites.
SecureML: A system for scalable privacy-preserving machine learning. In 2017 38th IEEE Symposium on Security and Privacy (SP) . IEEE, 19–38
Payman Mohassel and Yupeng Zhang. 2017 · 2017
Later among the works it cites.
Dynamic differential privacy for ADMM-based distributed classification learning
Tao Zhang and Quanyan Zhu. 2017 · 2017
Later among the works it cites.
Privacy-Preserving Logistic Regression Training
Charlotte Bonte and Frederik Vercauteren. 2018 · 2018
Later among the works it cites.
Privacy preserving federated big data analysis
Wenrui Dai, Shuang Wang, Hongkai Xiong, and Xiaoqian Jiang. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . ACM, 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In International Conference on Machine Learning . 201–210
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016 · 2016
Cited alongside, same era.
Learning privately from multiparty data. In International Conference on Machine Learning . 555–563
Jihun Hamm, Yingjun Cao, and Mikhail Belkin. 2016 · 2016
Cited alongside, same era.
Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems
Mingyi Hong, Zhi-Quan Luo, and Meisam Razaviyayn. 2016 · 2016
Cited alongside, same era.
Sustainable Industrial Processes by Embedded Real-Time Quality Prediction
Marco Stolpe, Hendrik Blom, and Katharina Morik. 2016 · 2016
Cited alongside, same era.
Privacy preserving multi-party machine learning with homomorphic encryption. In 29th Annual Conference on Neural Information Processing Systems (NIPS)
Hassan Takabi, Ehsan Hesamifard, and Mehdi Ghasemi. 2016 · 2016
Cited alongside, same era.
A dual perturbation approach for differential private admm-based distributed empirical risk minimization. In Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security . ACM, 129–137
Tao Zhang and Quanyan Zhu. 2016a · 2016
Cited alongside, same era.
Distributed Ridge Regression with Feature Partitioning. In 2018 52nd Asilomar Conference on Signals, Systems, and Computers . IEEE, 1423–1427
Cristiano Gratton, Venkategowda Naveen KD, Reza Arablouei, and Stefan Werner. 2018 · 2018
Later among the works it cites.
Preserving Differential Privacy Between Features in Distributed Estimation
Christina Heinze-Deml, Brian McWilliams, and Nicolai Meinshausen. 2018 · 2018
Later among the works it cites.
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
Zonghao Huang, Rui Hu, Yanmin Gong, and Eric Chan-Tin. 2018 · 2018
Later among the works it cites.
Privacy-preserving multiple linear regression of vertically partitioned real medical datasets
Hiroaki Kikuchi, Chika Hamanaga, Hideo Yasunaga, Hiroki Matsui, Hideki Hashimoto, and Chun-I Fan. 2018 · 2018
Later among the works it cites.
Supervised Learning Under Distributed Features
Bicheng Ying, Kun Yuan, and Ali H Sayed. 2018 · 2018
Later among the works it cites.
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms. In International Conference on Machine Learning . 5791–5800
Xueru Zhang, Mohammad Mahdi Khalili, and Mingyan Liu. 2018 · 2018
Later among the works it cites.
LIBSVM Data: Classification (Binary Class)
[n.d.] · 2019
Closest in time.
FDML: A Collaborative Machine Learning Framework for Distributed Features. In Proceedings of KDD ’19 . ACM
Yaochen Hu, Di Niu, Jianming Yang, and Shengping Zhou. 2019 · 2019
Closest in time.
Global convergence of ADMM in nonconvex nonsmooth optimization
Yu Wang, Wotao Yin, and Jinshan Zeng. 2019 · 2019
Closest in time.
Admm based privacy-preserving decentralized optimization
Chunlei Zhang, Muaz Ahmad, and Yongqiang Wang. 2019 · 2019
Closest in time.