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Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Communication efficient distributed machine learning with the parameter server
Mu Li, David G Andersen, Alex J Smola, and Kai Yu · 2014
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Tiny imagenet visual recognition challenge
Hadi Pouransari and Saman Ghili · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters
Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, and Kurt Keutzer · 2016
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Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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EMNIST: Extending MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 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
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang, Fred Roosta, Peng Xu, and Michael W. Mahoney · 2018
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Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, and Nati Srebro · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, et al · 2019
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Communication trade-offs for synchronized distributed SGD with large step size
Kumar Kshitij Patel and Aymeric Dieuleveut · 2019
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, and H. Brendan McMahan · 2020
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Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor · 2020
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama · 2021
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Federated learning for predicting clinical outcomes in patients with COVID-19
Ittai Dayan, Holger R. Roth, et al · 2021
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Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 2019
Cited alongside, same era.
Federated reinforcement learning
Hankz Hankui Zhuo, Wenfeng Feng, Qian Xu, Qiang Yang, and Yufeng Lin · 2019
Cited alongside, same era.
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Reddy Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Jakkam Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Specificity-Preserving Federated Learning for MR Image Reconstruction
Chun-Mei Feng, Yunlu Yan, Huazhu Fu, Yong Xu, and Ling Shao · 2021
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Federated Noisy Client Learning
Li Li, Liang Gao, Huazhu Fu, Bo Han, Cheng-Zhong Xu, and Ling Shao · 2021
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A survey on security and privacy of federated learning
Viraaji Mothukuri, Reza M. Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava · 2021
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Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, et al · 2021
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Achieving linear speedup with partial worker participation in non-iid federated learning
Haibo Yang, Minghong Fang, and Jia Liu · 2021
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Personalized federated learning with first order model optimization
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M. Alvarez · 2021
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