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Data heterogeneity presents significant challenges for federated learning (FL).
Bayesian Nonparametric Federated Learning of Neural Networks, May 2019
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 1905
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On the convergence of successive substitution sampling
Mark J Schervish and Bradley P Carlin · 1992
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On coresets for k-means and k-median clustering
Sariel Har-Peled and Soham Mazumdar · 2004
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Approximating extent measures of points
Pankaj K Agarwal, Sariel Har-Peled, and Kasturi R Varadarajan · 2004
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Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization, July 2020
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor · 2007
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Iterative row sampling
Mu Li, Gary L Miller, and Richard Peng · 2013
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An introduction to Markov processes
Daniel W Stroock · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Input sparsity time low-rank approximation via ridge leverage score sampling
Michael B Cohen, Cameron Musco, and Christopher Musco · 2017
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Training gaussian mixture models at scale via coresets
Mario Lucic, Matthew Faulkner, Andreas Krause, and Dan Feldman · 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 optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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Bayesian coreset construction via greedy iterative geodesic ascent
Trevor Campbell and Tamara Broderick · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
Cited alongside, same era.
Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
Cited alongside, same era.
An efficient framework for clustered federated learning
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran · 2020
Cited alongside, same era.
Federated Optimization in Heterogeneous Networks, April 2020
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
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Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, and Bill Lin · 2022
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When do curricula work in federated learning?
Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, and Bill Lin · 2022
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Feddm: Iterative distribution matching for communication-efficient federated learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh · 2022
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Fedsynth: Gradient compression via synthetic data in federated learning
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Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth O. Stanley, and Jeffrey Clune · 2020
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Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds
Mohsen Joneidi, Saeed Vahidian, Ashkan Esmaeili, Weijia Wang, Nazanin Rahnavard, Bill Lin, and Mubarak Shah · 2020
Cited alongside, same era.
Coresets for estimating means and mean square error with limited greedy samples
Saeed Vahidian, Baharan Mirzasoleiman, and Alexander Cloninger · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Personalized federated learning by structured and unstructured pruning under data heterogeneity
Saeed Vahidian, Mahdi Morafah, and Bill Lin · 2021
Cited alongside, same era.
Model-Contrastive Federated Learning, March 2021
Qinbin Li, Bingsheng He, and Dawn Song · 2021
Cited alongside, same era.
Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
Cited alongside, same era.
Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, and Yue Liu · 2022
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Spectrum pursuit with residual descent for column subset selection problem: Theoretical guarantees and applications in deep learning
Saeed Vahidian, Mohsen Joneidi, Ashkan Esmaeili, Siavash Khodadadeh, Sharare Zehtabian, and Bill Lin · 2022
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
Later among the works it cites.
Umar Khalid, Hasan Iqbal, Saeed Vahidian, Jing Hua, and Chen Chen · 2023
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Federated virtual learning on heterogeneous data with local-global distillation, 2023
Chun-Yin Huang, Ruinan Jin, Can Zhao, Daguang Xu, and Xiaoxiao Li · 2023
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Generalizing dataset distillation via deep generative prior, 2023
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
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Flis: Clustered federated learning via inference similarity for non-iid data distribution
Mahdi Morafah, Saeed Vahidian, Weijia Wang, and Bill Lin · 2023
Closest in time.
Flis: Clustered federated learning via inference similarity for non-iid data distribution
Mahdi Morafah, Saeed Vahidian, Weijia Wang, and Bill Lin · 2023
Closest in time.