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The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.
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Geodesic flow kernel for unsupervised domain adaptation
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The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
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Unsupervised domain adaptation by backpropagation
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Revisiting batch normalization for practical domain adaptation
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
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Communication-efficient learning of deep networks from decentralized data
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Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Efficient decentralized deep learning by dynamic model averaging
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Adaptive batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Xiaodi Hou, and Jiaying Liu · 2018
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Towards understanding regularization in batch normalization
Ping Luo, Xinjiang Wang, Wenqi Shao, and Zhanglin Peng · 2018
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On the importance of single directions for generalization
Ari S Morcos, David GT Barrett, Neil C Rabinowitz, and Matthew Botvinick · 2018
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A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach · 2018
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How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Siloed federated learning for multi-centric histopathology datasets
Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier, and Eric W Tramel · 2020
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Flower: A friendly federated learning research framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane · 2020
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Optimization theory for relu neural networks trained with normalization layers, 2020
Yonatan Dukler, Quanquan Gu, and Guido Montúfar · 2020
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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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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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Morpho-mnist: Quantitative assessment and diagnostics for representation learning
Daniel C Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, and Ben Glocker · 2019
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Domain-specific batch normalization for unsupervised domain adaptation
Woong-Gi Chang, Tackgeun You, Seonguk Seo, Suha Kwak, and Bohyung Han · 2019
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The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B Gibbons · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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TensorFlow Federated: Machine Learning on Decentralized Data , 2020
Google · 2020
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Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, et al · 2020
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Distributed Learning Platform , 2020
Michael Kamp and Linara Adilova · 2020
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Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data
Quande Liu, Qi Dou, Lequan Yu, and Pheng Ann Heng · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 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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The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al · 2020
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Training (overparametrized) neural networks in near-linear time
Jan van den Brand, Binghui Peng, Zhao Song, and Omri Weinstein · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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Fedpd: A federated learning framework with optimal rates and adaptivity to non-iid data
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu · 2020
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