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Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data.
On the numerical determination of the best approximations in the chebyshev sense
L Veidinger · 1960
Earlier work this paper cites.
A short course on approximation theory
Neal L Carothers · 1998
Earlier work this paper cites.
A privacy-preserving protocol for neural-network-based computation
Mauro Barni, Claudio Orlandi, and Alessandro Piva · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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Privacy preserving back-propagation neural network learning made practical with cloud computing
Jiawei Yuan and Shucheng Yu · 2013
Earlier work this paper cites.
Faster private set intersection based on { \{ OT } \} extension
Benny Pinkas, Thomas Schneider, and Michael Zohner · 2014
Earlier work this paper cites.
Privacy preserving deep computation model on cloud for big data feature learning
Qingchen Zhang, Laurence T Yang, and Zhikui Chen · 2015
Earlier work this paper cites.
Scalable and secure logistic regression via homomorphic encryption
Yoshinori Aono, Takuya Hayashi, Le Trieu Phong, and Lihua Wang · 2016
Earlier work this paper cites.
Intel sgx explained
Victor Costan and Srinivas Devadas · 2016
Earlier work this paper cites.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Inductive representation learning on large graphs
Ying RexLeskovec Jure Hamiltonm, William L · 2017
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Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
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Semi-supervised classification with graph convolutional networks, 2017
Thomas N. Kipf and Max Welling · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Entity resolution and federated learning get a federated resolution
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2018
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Graph attention networks, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
Later among the works it cites.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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On polynomial approximations for privacy-preserving and verifiable relu networks
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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Inductive representation learning on large graphs
Hamilton Will, Ying Zhitao, and Leskovec Jure · 2017
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A survey on homomorphic encryption schemes: Theory and implementation
Abbas Acar, Hidayet Aksu, A Selcuk Uluagac, and Mauro Conti · 2018
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Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
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Privacy partitioning: Protecting user data during the deep learning inference phase
Jianfeng Chi, Emmanuel Owusu, Xuwang Yin, Tong Yu, William Chan, Patrick Tague, and Yuan Tian · 2018
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So Jinhyun Avestimehr A. Salman Ali, Ramy E · 2020
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Secure social recommendation based on secret sharing
Chaochao Chen, Liang Li, Bingzhe Wu, Cheng Hong, Li Wang, and Jun Zhou · 2020
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Fede: Embedding knowledge graphs in federated setting
Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, and Huajun Chen · 2020
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Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S Yu · 2020
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Threats to federated learning: A survey
Lingjuan Lyu, Han Yu, and Qiang Yang · 2020
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