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It is widely believed that sharing gradients will not leak private training data in distributed learning systems such as Collaborative Learning and Federated Learning, etc.
On the limited memory bfgs method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Cited alongside, same era.
Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage · 2017
Cited alongside, same era.
Collaborative learning for deep neural networks
Guocong Song and Wei Chai · 2018
Cited alongside, same era.
Inference attacks against collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2018
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Later among the works it cites.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
Later among the works it cites.
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