LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning

Differential privacy

Cynthia Dwork · 2006

Earlier work this paper cites.

Boosting and differential privacy

Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan · 2010

Earlier work this paper cites.

Large scale distributed deep networks

Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew Senior, and Paul Tucker · 2013

Earlier work this paper cites.

Distributed autonomous online learning: Regrets and intrinsic privacy-preserving properties

Feng Yan, Shreyas Sundaram, SVN Vishwanathan, and Yuan Qi · 2013

Earlier work this paper cites.

Petuum: A new platform for distributed machine learning on big data

Eric P. Xing, Qirong Ho, Wei Dai, Kyu Kim Jin, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, and Yaoliang Yu · 2015

Cited alongside, same era.

Deep learning with elastic averaging sgd

Sixin Zhang, Anna E Choromanska, and Yann LeCun · 2015

Cited alongside, same era.

Adding gradient noise improves learning for very deep networks

Original

Arvind Neelakantan, Luke Vilnis, Quoc V Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens · 2015

Cited alongside, same era.

Tensorflow: A system for large-scale machine learning

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Davis, et al · 2016

Cited alongside, same era.

Deep learning with differential privacy

Martin Abadi, Andy Chu, Ian J Goodfellow, H Brendan Mcmahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016

Later among the works it cites.

Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent

Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu · 2017

Later among the works it cites.

cpsgd: Communication-efficient and differentially-private distributed sgd

Original

Naman Agarwal, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, and H Brendan Mcmahan · 2018

Closest in time.

Personalized and private peer-to-peer machine learning

Aurelien Bellet, Rachid Guerraoui, Mahsa Taziki, and Marc Tommasi · 2018

Closest in time.