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Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions.
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Deep Residual Learning for Image Recognition
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Federated Optimization: Distributed Machine Learning for On-Device Intelligence
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 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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Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
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On First-Order Meta-Learning Algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Jianyu Wang and Gauri Joshi · 2018
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Yuxin Wu and Kaiming He · 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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Provable guarantees for gradient-based meta-learning
Maria-Florina Balcan, Mikhail Khodak, and Ameet Talwalkar · 2019
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Qsparse-local-sgd: Distributed SGD with quantization, sparsification and local computations
Debraj Basu, Deepesh Data, Can Karakus, and Suhas Diggavi · 2019
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LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2019
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Federated Meta-Learning with Fast Convergence and Efficient Communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2019
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The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B. Gibbons · 2019
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Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Introducing TensorFlow Federated, 2019
Alex Ingerman and Krzys Ostrowski · 2019
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Improving Federated Learning Personalization via Model Agnostic Meta Learning
Yihan Jiang, Jakub Konečný, Keith Rush, and Sreeram Kannan · 2019
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Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 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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Model Pruning Enables Efficient Federated Learning on Edge Devices
Yuang Jiang, Shiqiang Wang, Victor Valls, Bong Jun Ko, Wei-Han Lee, Kin K. Leung, and Leandros Tassiulas · 2020
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SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh · 2020
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Tighter Theory for Local SGD on Identical and Heterogeneous Data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
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A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, and Sebastian U. Stich · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Cited alongside, same era.
Local SGD converges fast and communicates little
Sebastian U. Stich · 2019
Cited alongside, same era.
Federated Evaluation of On-device Personalization
Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, and Daniel Ramage · 2019
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On the computation and communication complexity of parallel SGD with dynamic batch sizes for stochastic non-convex optimization
Hao Yu and Rong Jin · 2019
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On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization
Hao Yu, Rong Jin, and Sen Yang · 2019
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Alekh Agarwal, John Langford, and Chen-Yu Wei · 2020
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Optimal Gradient Compression for Distributed and Federated Learning
Alyazeed Albasyoni, Mher Safaryan, Laurent Condat, and Peter Richtárik · 2020
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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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Ensemble Distillation for Robust Model Fusion in Federated Learning
Tao Lin, Lingjing Kong, Sebastian U. Stich, and Martin Jaggi · 2020
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PAC Identifiability in Federated Personalization
Ben London · 2020
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FedSplit: An algorithmic framework for fast federated optimization
Reese Pathak and Martin J. Wainwright · 2020
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Robust federated learning: The case of affine distribution shifts
Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, and Ali Jadbabaie · 2020
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A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
Jinhyun So, Basak Guler, and Salman Avestimehr · 2020
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Election coding for distributed learning: Protecting SignSGD against byzantine attacks
Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, and Jaekyun Moon · 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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Minibatch vs Local SGD for Heterogeneous Distributed Learning
Blake Woodworth, Kumar Kshitij Patel, and Nathan Srebro · 2020
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Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning
Semih Yagli, Alex Dytso, and H. Vincent Poor · 2020
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Salvaging Federated Learning by Local Adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
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Federated Accelerated Stochastic Gradient Descent
Honglin Yuan and Tengyu Ma · 2020
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FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu · 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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Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, and Afshin Rostamizadeh · 2021
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FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Hong-You Chen and Wei-Lun Chao · 2021
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Sharp bounds for federated averaging (local sgd) and continuous perspective
Margalit Glasgow, Honglin Yuan, and Tengyu Ma · 2021
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FL-NTK: A neural tangent kernel-based framework for federated learning analysis
Baihe Huang, Xiaoxiao Li, Zhao Song, and Xin Yang · 2021
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Distributed Second Order Methods with Fast Rates and Compressed Communication
Rustem Islamov, Xun Qian, and Peter Richtárik · 2021
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FedBN: Federated learning on Non-IID features via local batch normalization
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Adaptive Federated Optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H. Brendan McMahan · 2021
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Federated Reconstruction: Partially Local Federated Learning
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A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
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Federated Composite Optimization
Honglin Yuan, Manzil Zaheer, and Sashank Reddi · 2021
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