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Federated learning aims to learn a global model that performs well on client devices with limited cross-client communication.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
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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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Bayesian classification with Gaussian processes
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Thomas Peter Minka · 2001
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PAC-Bayesian generalisation error bounds for Gaussian process classification
Matthias Seeger · 2002
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PAC-Bayesian stochastic model selection
David A McAllester · 2003
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In defense of one-vs-all classification
Ryan Rifkin and Aldebaro Klautau · 2004
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A unifying view of sparse approximate Gaussian process regression
Joaquin Quinonero-Candela and Carl Edward Rasmussen · 2005
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Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
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Sparse Gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2006
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k-means++ the advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Bayesian inference for logistic models using Pólya–Gamma latent variables
Nicholas G. Polson, James G. Scott, and Jesse Windle · 2013
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Nonlinear time series: Theory, methods and applications with R examples
Randal Douc, Eric Moulines, and David Stoffer · 2014
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Privacy aware learning
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2014
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Scalable variational Gaussian process classification
James Hensman, Alexander Matthews, and Zoubin Ghahramani · 2015
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Dependent multinomial models made easy: stick breaking with the Pólya-Gamma augmentation
Scott W Linderman, Matthew J Johnson, and Ryan P Adams · 2015
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Obtaining well calibrated probabilities using Bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Manifold Gaussian processes for regression
Roberto Calandra, Jan Peters, Carl Edward Rasmussen, and Marc Peter Deisenroth · 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 optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing · 2016
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Stochastic variational deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P Xing · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Meta-SGD: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 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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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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Partitioned variational inference: A unified framework encompassing federated and continual learning
Thang D Bui, Cuong V Nguyen, Siddharth Swaroop, and Richard E Turner · 2018
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CINIC-10 is not Imagenet or CIFAR-10
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
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On the convergence of federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Multi-class Gaussian process classification made conjugate: Efficient inference via data augmentation
Théo Galy-Fajou, Florian Wenzel, Christian Donner, and Manfred Opper · 2020
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Automated augmented conjugate inference for non-conjugate gaussian process models
Théo Galy-Fajou, Florian Wenzel, and Manfred Opper · 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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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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Federated generalized Bayesian learning via distributed Stein variational gradient descent
Rahif Kassab and Osvaldo Simeone · 2020
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Learning Gaussian processes by minimizing PAC-Bayesian generalization bounds
David Reeb, Andreas Doerr, Sebastian Gerwinn, and Barbara Rakitsch · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Local SGD converges fast and communicates little
Sebastian U Stich · 2018
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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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On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimization
Fan Zhou and Guojing Cong · 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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Alpha MAML: Adaptive model-agnostic meta-learning
Harkirat Singh Behl, Atılım Güneş Baydin, and Philip HS Torr · 2019
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Survey of personalization techniques for federated learning
V. Kulkarni, Milind Kulkarni, and A. Pant · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B Allen, Randy P Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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Three approaches for personalization with applications to federated learning
Y. Mansour, M. Mohri, J. Ro, and A. T. Suresh · 2020
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FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2020
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Personalized federated learning with Moreau envelopes
Canh T Dinh, Nguyen Tran, and Tuan Dung Nguyen · 2020
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FedLoc: Federated learning framework for data-driven cooperative localization and location data processing
Feng Yin, Zhidi Lin, Qinglei Kong, Yue Xu, Deshi Li, Sergios Theodoridis, and Shuguang Robert Cui · 2020
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Personalized federated learning with first order model optimization
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M Alvarez · 2020
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Federated heavy hitters discovery with differential privacy
Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, and Wei Li · 2020
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A survey on federated learning: The journey from centralized to distributed on-site learning and beyond
Sawsan Abdulrahman, Hanine Tout, Hakima Ould-Slimane, Azzam Mourad, Chamseddine Talhi, and Mohsen Guizani · 2021
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GP-Tree: A Gaussian process classifier for few-shot incremental learning
Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, and Ethan Fetaya · 2021
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Towards ubiquitous learning: A first measurement of on-device training performance
Dongqi Cai, Qipeng Wang, Yuanqiang Liu, Yunxin Liu, Shangguang Wang, and Mengwei Xu · 2021
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A theorem of the alternative for personalized federated learning
Shuxiao Chen, Qinqing Zheng, Qi Long, and Weijie J Su · 2021
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FedU: A unified framework for federated multi-task learning with Laplacian regularization
Canh T Dinh, Tung T Vu, Nguyen H Tran, Minh N Dao, and Hongyu Zhang · 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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A Bayesian federated learning framework with multivariate gaussian product
Liangxi Liu and Feng Zheng · 2021
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Fast adaptation with linearized neural networks
Wesley Maddox, Shuai Tang, Pablo Moreno, Andrew Gordon Wilson, and Andreas Damianou · 2021
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A survey on security and privacy of federated learning
Viraaji Mothukuri, Reza M Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava · 2021
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The promises and pitfalls of deep kernel learning
Sebastian W. Ober, Carl E. Rasmussen, and Mark van der Wilk · 2021
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Personalized federated learning using hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik · 2021
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Bayesian few-shot classification with one-vs-each Pólya-Gamma augmented Gaussian processes
Jake Snell and Richard Zemel · 2021
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Towards personalized federated learning
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Fedgp: Correlation-based active client selection for heterogeneous federated learning
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Multi-class Gaussian process classification with noisy inputs
Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C Garrido-Merchán, and Daniel Hernández-Lobato · 2021
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A survey on federated learning
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