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Federated learning~(FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communication constraints.
The mnist database of handwritten digits
Yann LeCun · 1998
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Frank D McSherry · 2009
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A firm foundation for private data analysis
Cynthia Dwork · 2011
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Super-samples from kernel herding
Yutian Chen, Max Welling, and Alex Smola · 2012
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Project adam: Building an efficient and scalable deep learning training system
Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Communication-efficient distributed optimization using an approximate newton-type method
Ohad Shamir, Nati Srebro, and Tong Zhang · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
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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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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
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cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
Cited alongside, same era.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2020
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2020
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Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune · 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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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Cited alongside, same era.
Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation
Yang Chen, Xiaoyan Sun, and Yaochu Jin · 2019
Cited alongside, same era.
Neel Guha, Ameet Talwalkar, and Virginia Smith · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
Cited alongside, same era.
Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
Cited alongside, same era.
Order optimal one-shot distributed learning
Arsalan Sharifnassab, Saber Salehkaleybar, and S Jamaloddin Golestani · 2019
Cited alongside, same era.
Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2019
Cited alongside, same era.
Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
Cited alongside, same era.
Distilled one-shot federated learning
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu · 2020
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Communication-efficient federated learning
Mingzhe Chen, Nir Shlezinger, H Vincent Poor, Yonina C Eldar, and Shuguang Cui · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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A survey on federated learning systems: vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He · 2021
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Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
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Hyperparameter tuning with renyi differential privacy
Nicolas Papernot and Thomas Steinke · 2021
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One-shot federated learning: theoretical limits and algorithms to achieve them
Saber Salehkaleybar, Arsalan Sharifnassab, and S Jamaloddin Golestani · 2021
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Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2021
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Federated learning on non-iid data silos: An experimental study
Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He · 2022
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Cafe: Learning to condense dataset by aligning features
Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You · 2022
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