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We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data sources subject to communication and privacy constraints.
Stochastic approximation with two time scales
Vivek S Borkar · 1997
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Nonlinear systems
Hassan K Khalil · 2002
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Convergence rate of linear two-time-scale stochastic approximation
Vijay R Konda, John N Tsitsiklis, et al · 2004
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Stochastic approximation: a dynamical systems viewpoint , volume 48
Vivek S Borkar · 2009
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Characterization and computation of local nash equilibria in continuous games
Lillian J Ratliff, Samuel A Burden, and S Shankar Sastry · 2013
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Bandwidth analysis of smart meter network infrastructure
Kartheepan Balachandran, Rasmus L Olsen, and Jens M Pedersen · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Measuring the energy intensity of domestic activities from smart meter data
Lina Stankovic, V Stankovic, J Liao, and C Wilson · 2016
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Two time-scale stochastic approximation with controlled markov noise and off-policy temporal-difference learning
Prasenjit Karmakar and Shalabh Bhatnagar · 2017
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Jianyu Wang and Gauri Joshi · 2018
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Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2018
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Generative models for effective ml on private, decentralized datasets
Sean Augenstein, H Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, et al · 2019
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Benjamin Chasnov, Lillian J Ratliff, Eric Mazumdar, and Samuel A Burden · 2019
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Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 2017
Cited alongside, same era.
The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Cited alongside, same era.
Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
Cited alongside, same era.
Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
Cited alongside, same era.
Gan augmentation: Augmenting training data using generative adversarial networks
Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdés Hernández, Joanna Wardlaw, and Daniel Rueckert · 2018
Cited alongside, same era.
Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Cited alongside, same era.
Local sgd converges fast and communicates little
Sebastian U Stich · 2018
Cited alongside, same era.
Canh Dinh, Nguyen H Tran, Minh NH Nguyen, Choong Seon Hong, Wei Bao, Albert Zomaya, and Vincent Gramoli · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Federated ai lets a team imagine together: Federated learning of gans
A Rajagopal. and V. Nirmala · 2019
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Adaptive federated learning in resource constrained edge computing systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Decentralized learning of generative adversarial networks from multi-client non-iid data
Ryo Yonetani, Tomohiro Takahashi, Atsushi Hashimoto, and Yoshitaka Ushiku · 2019
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
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