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Federated learning is a method of training models on private data distributed over multiple devices.
The association of income and education for males by region, race, and age
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Anders Krogh and John A Hertz · 1992
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Bagging predictors
Leo Breiman · 1996
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Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid
Ron Kohavi · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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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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Regularized multi–task learning
Theodoros Evgeniou and Massimiliano Pontil · 2004
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Differential privacy
Cynthia Dwork · 2006
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Introduction to Linear Regression Analysis (4th ed.)
Douglas C. Montgomery, Elizabeth A. Peck, and Geoffrey G. Vining · 2006
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Learning fair representations
Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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VQA: Visual Question Answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh · 2015
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Compressing neural networks with the hashing trick
Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Man is to computer programmer as woman is to homemaker? Debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
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Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Communication-efficient distributed learning of discrete distributions
Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, and Ludwig Schmidt · 2017
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Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research
Joseph C Franklin, Jessica D Ribeiro, Kathryn R Fox, Kate H Bentley, Evan M Kleiman, Xieyining Huang, Katherine M Musacchio, Adam C Jaroszewski, Bernard P Chang, and Matthew K Nock · 2017
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In-patient suicide: selection of people at risk, failure of protection and the possibility of causation
Matthew Michael Large, Daniel Thomas Chung, Michael Davidson, Mark Weiser, and Christopher James Ryan · 2017
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Learning to pivot with adversarial networks
Gilles Louppe, Michael Kagan, and Kyle Cranmer · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
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Distributed mean estimation with limited communication
Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, and H. Brendan McMahan · 2017
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Efficient distributed learning with sparsity
Jialei Wang, Mladen Kolar, Nathan Srebro, and Tong Zhang · 2017
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Demystifying parallel and distributed deep learning: An in-depth concurrency analysis
Adversarial removal of gender from deep image representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez · 2018
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Multimodal machine learning: A survey and taxonomy
Tadas Baltrusaitis, Chaitanya Ahuja, and Louis-Philippe Morency · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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Improved adversarial learning for fair classification
L. Elisa Celis and Vijay Keswani · 2019
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Tal Ben-Nun and Torsten Hoefler · 2018
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Expanding the reach of federated learning by reducing client resource requirements
Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, and Ameet Talwalkar · 2018
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LEAF: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Why is my classifier discriminatory?
Irene Chen, Fredrik D Johansson, and David Sontag · 2018
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Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg · 2018
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William Dally · 2018
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Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, and Chunping Wang · 2019
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Neel Guha, Ameet Talwalkar, and Virginia Smith · 2019
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Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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Strong and simple baselines for multimodal utterance embeddings
Paul Pu Liang, Yao Chong Lim, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger · 2019
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Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit
Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2019
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The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei and Andrea Montanari · 2019
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Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Hardware/software security patches for internet of trillions of things
John A. Stankovic, Tu Le, Abdeltawab M. Hendawi, and Yuan Tian · 2019
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Inherent tradeoffs in learning fair representation
Han Zhao and Geoffrey J. Gordon · 2019
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Gender bias in contextualized word embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang · 2019
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Quantized epoch-sgd for communication-efficient distributed learning
Shen-Yi Zhao, Hao Gao, and Wu-Jun Li · 2019
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Towards debiasing sentence representations
Paul Pu Liang, Irene Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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Threats to federated learning: A survey
Lingjuan Lyu, Han Yu, and Qiang Yang · 2020
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Learning not to learn in the presence of noisy labels, 2020
Liu Ziyin, Blair Chen, Ru Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2020
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