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Federated learning is an emerging technique for training models from decentralized data sets.
Rule-based machine learning methods for functional prediction
Sholom M Weiss and Nitin Indurkhya · 1995
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Regression using classification algorithms
Luis Torgo and Joao Gama · 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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Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
Jock A Blackard and Denis J Dean · 1999
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A survey of cross-validation procedures for model selection
Sylvain Arlot and Alain Celisse · 2010
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Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Harnessing deep neural networks with logic rules
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, and Eric Xing · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Julia: A fresh approach to numerical computing
Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B Shah · 2017
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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
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 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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Data fusion and machine learning for industrial prognosis: Trends and perspectives towards industry 4.0
Alberto Diez-Olivan, Javier Del Ser, Diego Galar, and Basilio Sierra · 2019
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Towards efficient and privacy-preserving federated deep learning
Meng Hao, Hongwei Li, Guowen Xu, Sen Liu, and Haomiao Yang · 2019
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan · 2019
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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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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Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh · 2020
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Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen Tran, and Josh Nguyen · 2020
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Gradient-based inference for networks with output constraints
Jay Yoon Lee, Sanket Vaibhav Mehta, Michael Wick, Jean-Baptiste Tristan, and Jaime Carbonell · 2019
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A primal dual formulation for deep learning with constraints
Yatin Nandwani, Abhishek Pathak, and Parag Singla · 2019
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Informed machine learning–a taxonomy and survey of integrating knowledge into learning systems
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Federated evaluation of on-device personalization
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Improving deep learning models via constraint-based domain knowledge: a brief survey
Andrea Borghesi, Federico Baldo, and Michela Milano · 2020
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Federated learning with differential privacy: Algorithms and performance analysis
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor · 2020
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The dawning of a new era in applied mathematics
Weinan E · 2021
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Personalized cross-silo federated learning on non-iid data
Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang · 2021
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Informed machine learning - a taxonomy and survey of integrating prior knowledge into learning systems
Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, and Jannis Schuecker · 2021
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Integration of knowledge and data in machine learning
Yuntian Chen and Dongxiao Zhang · 2022
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