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Federated machine learning which enables resource constrained node devices (e.g., mobile phones and IoT devices) to learn a shared model while keeping the training data local, can provide privacy, security and economic benefits by designing an effective communication protocol.
Can machine learning be secure?
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar · 2006
Earlier work this paper cites.
Accelerated projected gradient method for linear inverse problems with sparsity constraints
Ingrid Daubechies, Massimo Fornasier, and Ignace Loris · 2008
Earlier work this paper cites.
Privacy-preserving gradient-descent methods
Shuguo Han, Wee Keong Ng, Li Wan, and Vincent CS Lee · 2009
Earlier work this paper cites.
The security of machine learning
Marco Barreno, Blaine Nelson, Anthony D Joseph, and J Doug Tygar · 2010
Earlier work this paper cites.
Adversarial machine learning
Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and JD Tygar · 2011
Earlier work this paper cites.
Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Return of the devil in the details: Delving deep into convolutional nets
Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2014
Cited alongside, same era.
Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
Cited alongside, same era.
Data poisoning attacks against autoregressive models
Scott Alfeld, Xiaojin Zhu, and Paul Barford · 2016
Cited alongside, same era.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
Cited alongside, same era.
Data poisoning attacks on factorization-based collaborative filtering
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik · 2016
Cited alongside, same era.
Coupled multi-layer attentions for co-extraction of aspect and opinion terms
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao · 2017
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2018
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Data poisoning attacks on multi-task relationship learning
Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, and Sinno Jialin Pan · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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Verifynet: Secure and verifiable federated learning
Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin · 2019
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Federated machine learning: Concept and applications
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Optimizing personalized email filtering thresholds to mitigate sequential spear phishing attacks
Mengchen Zhao, Bo An, and Christopher Kiekintveld · 2016
Cited alongside, same era.
Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
Cited alongside, same era.
Secure linear regression on vertically partitioned datasets
Adrià Gascón, Phillipp Schoppmann, and Mariana Raykova
Cited in the paper.
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Later among the works it cites.
Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
Later among the works it cites.