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Federated learning is an emerging distributed machine learning framework which jointly trains a global model via a large number of local devices with data privacy protections.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2010
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2010
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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 learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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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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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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Group normalization
Yuxin Wu and Kaiming He · 2018
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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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First analysis of local gd on heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2019
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Feddane: A federated newton-type method
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smithy · 2019
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Variance reduced local sgd with lower communication complexity
Xianfeng Liang, Shuheng Shen, Jingchang Liu, Zhen Pan, Enhong Chen, and Yifei Cheng · 2019
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Slowmo: Improving communication-efficient distributed sgd with slow momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, and Michael Rabbat · 2019
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Federated learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu · 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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Fedopt: Towards communication efficiency and privacy preservation in federated learning
Muhammad Asad, Ahmed Moustafa, and Takayuki Ito · 2020
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Fedbe: Making bayesian model ensemble applicable to federated learning
Hong-You Chen and Wei-Lun Chao · 2020
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Toward communication efficient adaptive gradient method
Xiangyi Chen, Xiaoyun Li, and Ping Li · 2020
Cited alongside, same era.
Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 2020
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The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons · 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
Cited alongside, same era.
From local sgd to local fixed-point methods for federated learning
Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, and Peter Richtarik · 2020
Cited alongside, same era.
Achieving linear speedup with partial worker participation in non-iid federated learning
Haibo Yang, Minghong Fang, and Jia Liu · 2021
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Fedpd: A federated learning framework with adaptivity to non-iid data
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu · 2021
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Towards understanding sharpness-aware minimization
Maksym Andriushchenko and Nicolas Flammarion · 2022
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Improving generalization in federated learning by seeking flat minima
Debora Caldarola, Barbara Caputo, and Marco Ciccone · 2022
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Efficient-adam: Communication-efficient distributed adam with complexity analysis
Congliang Chen, Li Shen, Wei Liu, and Zhi-Quan Luo · 2022
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Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
Cited alongside, same era.
Exploiting the surrogate gap in online multiclass classification
Dirk van der Hoeven · 2020
Cited alongside, same era.
On second-order optimization methods for federated learning
Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, and Sebastian U Stich · 2021
Cited alongside, same era.
Convergence and accuracy trade-offs in federated learning and meta-learning
Zachary Charles and Jakub Konečnỳ · 2021
Cited alongside, same era.
Quantized adam with error feedback
Congliang Chen, Li Shen, Haozhi Huang, and Wei Liu · 2021
Cited alongside, same era.
Federated learning based on dynamic regularization
Alp Emre Durmus, Zhao Yue, Matas Ramon, Mattina Matthew, Whatmough Paul, and Saligrama Venkatesh · 2021
Cited alongside, same era.
Federated learning with compression: Unified analysis and sharp guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi · 2021
Cited alongside, same era.
Liang Gao, Huazhu Fu, Li Li, Yingwen Chen, Ming Xu, and Cheng-Zhong Xu · 2022
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Fedadmm: A robust federated deep learning framework with adaptivity to system heterogeneity
Yonghai Gong, Yichuan Li, and Nikolaos M Freris · 2022
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Communication-efficient federated learning with acceleration of global momentum
Geeho Kim, Jinkyu Kim, and Bohyung Han · 2022
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From distributed machine learning to federated learning: A survey
Ji Liu, Jizhou Huang, Yang Zhou, Xuhong Li, Shilei Ji, Haoyi Xiong, and Dejing Dou · 2022
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Generalized federated learning via sharpness aware minimization
Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, and Zhuo Lu · 2022
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Fedproto: Federated prototype learning across heterogeneous clients
Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang · 2022
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Fedadmm: A federated primal-dual algorithm allowing partial participation
Han Wang, Siddartha Marella, and James Anderson · 2022
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Over-the-air federated learning via second-order optimization
Peng Yang, Yuning Jiang, Ting Wang, Yong Zhou, Yuanming Shi, and Colin N Jones · 2022
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Penalizing gradient norm for efficiently improving generalization in deep learning
Yang Zhao, Hao Zhang, and Xiuyuan Hu · 2022
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Improving sharpness-aware minimization with fisher mask for better generalization on language models
Qihuang Zhong, Liang Ding, Li Shen, Peng Mi, Juhua Liu, Bo Du, and Dacheng Tao · 2022
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Enhance local consistency in federated learning: A multi-step inertial momentum approach
Yixing Liu, Yan Sun, Zhengtao Ding, Li Shen, Bo Liu, and Dacheng Tao · 2023
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Improving the model consistency of decentralized federated learning
Yifan Shi, Li Shen, Kang Wei, Yan Sun, Bo Yuan, Xueqian Wang, and Dacheng Tao · 2023
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