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Federated learning is used for decentralized training of machine learning models on a large number (millions) of edge mobile devices.
No training required: Exploring random encoders for sentence classification
John Wieting and Douwe Kiela · 1901
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Adaptive nonlinear system identification with echo state networks
Herbert Jaeger · 2002
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Real-time computing without stable states: A new framework for neural computation based on perturbations
Wolfgang Maass, Thomas Natschläger, and Henry Markram · 2002
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Neural Network Learning: Theoretical Foundations
Martin Anthony and Peter L. Bartlett · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Bypassing the ambient dimension: Private sgd with gradient subspace identification
Yingxue Zhou, Zhiwei Steven Wu, and Arindam Banerjee · 2009
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Do deep nets really need to be deep?, 2014
Lei Jimmy Ba and Rich Caruana · 2014
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Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Trends in extreme learning machines: A review
Gao Huang, Guang-Bin Huang, Shiji Song, and Keyou You · 2015
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Deep neural networks with random gaussian weights: A universal classification strategy?
Raja Giryes, Guillermo Sapiro, and Alex M. Bronstein · 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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Emnist: an extension of mnist to handwritten letters, 2017
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Attention is all you need, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
Cited alongside, same era.
Towards understanding the role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro · 2018
Cited alongside, same era.
Intriguing properties of randomly weighted networks: Generalizing while learning next to nothing, 2018
Amir Rosenfeld and John K. Tsotsos · 2018
Cited alongside, same era.
Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
Cited alongside, same era.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Training production language models without memorizing user data
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays · 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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The impact of neural network overparameterization on gradient confusion and stochastic gradient descent
Karthik Abinav Sankararaman, Soham De, Zheng Xu, W Ronny Huang, and Tom Goldstein · 2020
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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
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Cited alongside, same era.
Generating long sequences with sparse transformers, 2019
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2019
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification, 2019
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
Cited alongside, same era.
Tensorflow federated
Alex Ingerman and Krzys Ostrowski · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Cited alongside, same era.
Are all layers created equal?, 2019
Chiyuan Zhang, Samy Bengio, and Yoram Singer · 2019
Cited alongside, same era.
Micah J Sheller, Brandon Edwards, G Anthony Reina, Jason Martin, Sarthak Pati, Aikaterini Kotrotsou, Mikhail Milchenko, Weilin Xu, Daniel Marcus, Rivka R Colen, et al · 2020
Later among the works it cites.
Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2020
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Linformer: Self-attention with linear complexity, 2020
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
Later among the works it cites.
Rethinking attention with performers, 2021
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, and Adrian Weller · 2021
Closest in time.
A practical survey on faster and lighter transformers, 2021
Quentin Fournier, Gaétan Marceau Caron, and Daniel Aloise · 2021
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Training batchnorm and only batchnorm: On the expressive power of random features in cnns, 2021
Jonathan Frankle, David J. Schwab, and Ari S. Morcos · 2021
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Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos I Venieris, and Nicholas D Lane · 2021
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Fnet: Mixing tokens with fourier transforms, 2021
James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, and Santiago Ontanon · 2021
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Reservoir transformers, 2021
Sheng Shen, Alexei Baevski, Ari S. Morcos, Kurt Keutzer, Michael Auli, and Douwe Kiela · 2021
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Echo state speech recognition
Harsh Shrivastava, Ankush Garg, Yuan Cao, Yu Zhang, and Tara Sainath · 2021
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Federated reconstruction: Partially local federated learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash · 2021
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