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The standard objective in machine learning is to train a single model for all users.
Federated learning of out-of-vocabulary words
Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Françoise Beaufays · 1903
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Principles of risk minimization for learning theory
Vladimir Vapnik · 1992
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Nist special database 19 handprinted forms and characters database
Patrick J Grother · 1995
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Hierarchical bayes models: A practitioners guide. ssrn scholarly paper id 655541
Greg M Allenby, Peter E Rossi, and Robert E McCulloch · 2005
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Clustering with Bregman divergences
Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, and Joydeep Ghosh · 2005
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Multilevel (hierarchical) modeling: what it can and cannot do
Andrew Gelman · 2006
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Learning bounds for domain adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman · 2008
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Data clustering: 50 years beyond k k -means
Anil K Jain · 2010
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New analysis and algorithm for learning with drifting distributions
Mehryar Mohri and Andres Munoz Medina · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S 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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Recent progresses in deep learning based acoustic models
Dong Yu and Jinyu Li · 2017
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2017
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cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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Federated learning of predictive models from federated electronic health records
Theodora S. Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch. Paschalidis, and Wei Shi · 2018
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Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Federated meta-learning with fast convergence and efficient communication
Scaffold: Stochastic controlled averaging for on-device federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2019
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Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina F Balcan, and Ameet S Talwalkar · 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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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Private federated learning with domain adaptation
Daniel Peterson, Pallika Kanani, and Virendra J Marathe · 2019
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Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Federated learning for ultra-reliable low-latency v2v communications
Sumudu Samarakoon, Mehdi Bennis, Walid Saad, and Merouane Debbah · 2018
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Local SGD converges fast and communicates little
Sebastian U. Stich · 2018
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Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Blake E Woodworth, Jialei Wang, Adam Smith, H. Brendan McMahan, and Nati Srebro · 2018
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Applied federated learning: Improving Google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
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Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
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Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 2019
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Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 2019
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Federated evaluation of on-device personalization
Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, and Daniel Ramage · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Fully decentralized joint learning of personalized models and collaboration graphs
Valentina Zantedeschi, Aurélien Bellet, and Marc Tommasi · 2019
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Alekh Agarwal, John Langford, and Chen-Yu Wei · 2020
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Adaptive personalized federated learning
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 2020
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Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 2020
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Survey of personalization techniques for federated learning
Viraj Kulkarni, Milind Kulkarni, and Aniruddha Pant · 2020
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Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
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