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Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteeing the data privacy of the trainers.
Learning multiple layers of features from tiny images
Alex K. and H. Geoffrey · 2009
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Perturbed iterate analysis for asynchronous stochastic optimization
Horia Mania, Xinghao Pan, Dimitris Papailiopoulos, Benjamin Recht, Kannan Ramchandran, and Michael I Jordan · 2015
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 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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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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Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Local sgd converges fast and communicates little
Sebastian U · 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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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
Earlier work this paper cites.
Fedat: A communication-efficient federated learning method with asynchronous tiers under non-iid data
Zheng Chai, Yujing Chen, Liang Zhao, Yue Cheng, and Huzefa Rangwala · 2020
Cited alongside, same era.
Cryptonite: A framework for flexible time-series secure aggregation with online fault tolerance
Ryan Karl, Jonathan Takeshita, Nirajan Koirla, and Taeho Jung · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
Cited alongside, same era.
Generalization in nli: Ways (not) to go beyond simple heuristics, 2021
Prajjwal Bhargava, Aleksandr Drozd, and Anna Rogers · 2021
Cited alongside, same era.
Sapus: Self-adaptive parameter update strategy for dnn training on multi-gpu clusters
Zhaorui Zhang and Choli Wang · 2021
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Heterogeneous ensemble knowledge transfer for training large models in federated learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, and Dimitriadis · 2022
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Sharper convergence guarantees for asynchronous sgd for distributed and federated learning
Anastasiia Koloskova, Sebastian U Stich, and Martin Jaggi · 2022
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Federated learning with buffered asynchronous aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, and Dzmitry Huba · 2022
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How asynchronous can federated learning be?
Ningxin Su and Baochun Li · 2022
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Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
Cited alongside, same era.
Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
Jinhyun So, Başak Güler, and A Salman Avestimehr · 2021
Cited alongside, same era.
Mipd: An adaptive gradient sparsification framework for distributed dnns training
Zhaorui Zhang and Choli Wang · 2022
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Momentum-driven adaptive synchronization model for distributed dnn training on hpc clusters
Zhaorui Zhang, Zhuoran Ji, and Choli Wang · 2022
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A general theory for federated optimization with asynchronous and heterogeneous clients updates
Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi · 2023
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