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Federated learning is a distributed paradigm that allows multiple parties to collaboratively train deep models without exchanging the raw data.
The probable error of a mean
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Training with noise is equivalent to tikhonov regularization
Chris M Bishop · 1995
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Incorporating invariances in support vector learning machines
Bernhard Schölkopf, Chris Burges, and Vladimir Vapnik · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Vicinal risk minimization
Olivier Chapelle, Jason Weston, Léon Bottou, and Vladimir Vapnik · 2000
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Caltech-256 object category dataset
Gregory Griffin, Alex Holub, and Pietro Perona · 2007
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman · 2012
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Better mixing via deep representations
Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, and Salah Rifai · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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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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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
Cited alongside, same era.
Regularization for deep learning: A taxonomy
Jan Kukačka, Vladimir Golkov, and Daniel Cremers · 2017
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Robust federated learning: The case of affine distribution shifts
Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, and Ali Jadbabaie · 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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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le · 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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Sharpness-aware minimization for efficiently improving generalization
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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
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
Cited alongside, same era.
Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Cited alongside, same era.
Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
Cited alongside, same era.
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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Adaptive federated optimization
Sashank J Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
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Fedmix: Approximation of mixup under mean augmented federated learning
Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang · 2021
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Domain generalization with mixstyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2021
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Harmofl: Harmonizing local and global drifts in federated learning on heterogeneous medical images
Meirui Jiang, Zirui Wang, and Qi Dou · 2022
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Multi-level branched regularization for federated learning
Jinkyu Kim, Geeho Kim, and Bohyung Han · 2022
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Noisy feature mixup
Soon Hoe Lim, N Benjamin Erichson, Francisco Utrera, Winnie Xu, and Michael W Mahoney · 2022
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Generalized federated learning via sharpness aware minimization
Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, and Zhuo Lu · 2022
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What do we mean by generalization in federated learning?
Honglin Yuan, Warren Richard Morningstar, Lin Ning, and Karan Singhal · 2022
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