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State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions.
Slowmo: Improving communication-efficient distributed sgd with slow momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, and Michael Rabbat · 1910
Earlier work this paper cites.
Mime: Mimicking centralized stochastic algorithms in federated learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
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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 Bach, and Simon Lacoste-Julien · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Firecaffe: near-linear acceleration of deep neural network training on compute clusters
Forrest N Iandola, Matthew W Moskewicz, Khalid Ashraf, and Kurt Keutzer · 2016
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QSGD: Communication-efficient SGD via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Accurate, large minibatch SGD: Training Imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 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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Distributed mean estimation with limited communication
Ananda Theertha Suresh, X Yu Felix, Sanjiv Kumar, and H Brendan McMahan · 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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Mitigating sybils in federated learning poisoning
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, Brendan McMahan, et al · 2019
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Truth or backpropaganda? an empirical investigation of deep learning theory
Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, and Tom Goldstein · 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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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2019
Earlier work this paper cites.
Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Cited alongside, same era.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
Cited alongside, same era.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
Cited alongside, same era.
Adaptive personalized federated learning
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 2020
Cited alongside, same era.
Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Cited alongside, same era.
Towards model agnostic federated learning using knowledge distillation
Andrei Afonin and Sai Praneeth Karimireddy · 2021
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Federated learning and privacy: Building privacy-preserving systems for machine learning and data science on decentralized data
Kallista Bonawitz, Peter Kairouz, Brendan McMahan, and Daniel Ramage · 2021
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On large-cohort training for federated learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, and Virginia Smith · 2021
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Exploiting shared representations for personalized federated learning
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai · 2021
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Federated learning with compression: Unified analysis and sharp guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi · 2021
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Local model poisoning attacks to { \{ Byzantine-Robust } \} federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong · 2020
Cited alongside, same era.
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M Roy, and Surya Ganguli · 2020
Cited alongside, same era.
The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons · 2020
Cited alongside, same era.
Evaluation of neural architectures trained with square loss vs cross-entropy in classification tasks
Like Hui and Mikhail Belkin · 2020
Cited alongside, same era.
Nonrivalry and the economics of data
Charles I Jones and Christopher Tonetti · 2020
Cited alongside, same era.
Survey of personalization techniques for federated learning
Viraj Kulkarni, Milind Kulkarni, and Aniruddha Pant · 2020
Cited alongside, same era.
Participatory approaches to machine learning
Bogdan Kulynych, David Madras, Smitha Milli, Inioluwa Deborah Raji, Angela Zhou, and Richard Zemel · 2020
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, et al · 2021
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Byzantine-robust learning on heterogeneous datasets via bucketing
Sai Praneeth Karimireddy, Lie He, and Martin Jaggi · 2021
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Quasi-global momentum: Accelerating decentralized deep learning on heterogeneous data
Tao Lin, Sai Praneeth Karimireddy, Sebastian U Stich, and Martin Jaggi · 2021
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Properties of the after kernel
Philip M Long · 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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Quped: Quantized personalization via distillation with applications to federated learning
Kaan Ozkara, Navjot Singh, Deepesh Data, and Suhas Diggavi · 2021
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Building the New Economy: Data as Capital
Alex Pentland, Alexander Lipton, and Thomas Hardjono · 2021
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Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
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A survey of fairness-aware federated learning
Yuxin Shi, Han Yu, and Cyril Leung · 2021
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Fedproto: Federated prototype learning over heterogeneous devices
Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang · 2021
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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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Fed2: Feature-aligned federated learning
Fuxun Yu, Weishan Zhang, Zhuwei Qin, Zirui Xu, Di Wang, Chenchen Liu, Zhi Tian, and Xiang Chen · 2021
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Optimization with access to auxiliary information
El Mahdi Chayti and Sai Praneeth Karimireddy · 2022
Closest in time.
Flamby: Datasets and benchmarks for cross-silo federated learning in realistic settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, et al · 2022
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Byzantine-robust decentralized learning via self-centered clipping
Lie He, Sai Praneeth Karimireddy, and Martin Jaggi · 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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On the unreasonable effectiveness of federated averaging with heterogeneous data
Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, and Tong Zhang · 2022
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More than a toy: Random matrix models predict how real-world neural representations generalize
Alexander Wei, Wei Hu, and Jacob Steinhardt · 2022
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Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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