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Data heterogeneity across clients is a key challenge in federated learning.
An estimation of sensor energy consumption
Malka N. Halgamuge, Moshe Zukerman, Kotagiri Ramamohanarao, and Hai Le Vu · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
Aaron Defazio, Francis R. Bach, and Simon Lacoste-Julien · 2014
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Communication-efficient distributed optimization using an approximate newton-type method
Ohad Shamir, Nathan Srebro, and Tong Zhang · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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QSGD: randomized quantization for communication-optimal stochastic gradient descent
Dan Alistarh, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 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 Konečný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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On the convergence of federated optimization in heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
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Sparsified SGD with memory
Sebastian U. Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
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On the ineffectiveness of variance reduced optimization for deep learning
Aaron Defazio and Léon Bottou · 2019
Cited alongside, same era.
Better communication complexity for local SGD
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2019
Cited alongside, same era.
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey E. Hinton · 2019
Cited alongside, same era.
Distributed learning with compressed gradient differences
Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, and Peter Richtárik · 2019
Cited alongside, same era.
Unified optimal analysis of the (stochastic) gradient method
Sebastian U. Stich · 2019
Cited alongside, same era.
Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, and Venkatesh Saligrama · 2021
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Uncertainty sets for image classifiers using conformal prediction
Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, and Jitendra Malik · 2021
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Model-contrastive federated learning
Qinbin Li, Bingsheng He, and Dawn Song · 2021
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No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Mi Luo, Fei Chen, D. Hu, Yifan Zhang, Jian Liang, and Jiashi Feng · 2021
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Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth
Thao Nguyen, Maithra Raghu, and Simon Kornblith · 2021
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
Cited alongside, same era.
On the discrepancy between the theoretical analysis and practical implementations of compressed communication for distributed deep learning
Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, and Panos Kalnis · 2020
Cited alongside, same era.
SCAFFOLD: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
A unified theory of decentralized SGD with changing topology and local updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, and Sebastian Stich · 2020
Cited alongside, same era.
Acceleration for compressed gradient descent in distributed and federated optimization
Zhize Li, Dmitry Kovalev, Xun Qian, and Peter Richtárik · 2020
Cited alongside, same era.
Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U. Stich, and Martin Jaggi · 2020
Cited alongside, same era.
Classification with valid and adaptive coverage
Yaniv Romano, Matteo Sesia, and Emmanuel J. Candès · 2020
Cited alongside, same era.
Jaehoon Oh, Sangmook Kim, and Se-Young Yun · 2021
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Fedpage: A fast local stochastic gradient method for communication-efficient federated learning
Haoyu Zhao, Zhize Li, and Peter Richtárik · 2021
Later among the works it cites.
Fedavg with fine tuning: Local updates lead to representation learning
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai · 2022
Closest in time.
Feddc: Federated learning with non-iid data via local drift decoupling and correction
Liang Gao, Huazhu Fu, Li Li, Yingwen Chen, Ming Xu, and Cheng-Zhong Xu · 2022
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ProxSkip: Yes! local gradient steps provably lead to communication acceleration! finally!
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, and Peter Richtárik · 2022
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The error-feedback framework: Better rates for sgd with delayed gradients and compressed updates
Sebastian U. Stich and Sai Praneeth Karimireddy · 2022
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Minimizing client drift in federated learning via adaptive bias estimation
F Varno, M Saghayi, L Rafiee, S Gupta, S Matwin, and M Havaei · 2022
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TCT: Convexifying federated learning using bootstrapped neural tangent kernels
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, and Michael Jordan · 2022
Closest in time.