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Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.
Large scale distributed deep networks
Jeffrey Dean, Greg S Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V Le, Mark Z Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, et al · 2012
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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8-bit approximations for parallelism in deep learning
Tim Dettmers · 2015
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J Dally · 2017
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Deep models under the GAN: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
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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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Sparse communication for distributed gradient descent
Alham Fikri Aji and Kenneth Heafield · 2017
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Federated learning of predictive models from federated electronic health records
Theodora S. Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch. Paschalidis, and Wei Shi · 2018
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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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signsgd: Compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar · 2018
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Sebastian U Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
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A generic framework for privacy preserving deep learning, 2018
Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach · 2018
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LEAF: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Model pruning enables efficient federated learning on edge devices
Yuang Jiang, Shiqiang Wang, Bong-Jun Ko, Wei-Han Lee, and Leandros Tassiulas · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Cited alongside, same era.
Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan · 2019
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 · 2019
Cited alongside, same era.
Federated knowledge distillation
Hyowoon Seo, Jihong Park, Seungeun Oh, Mehdi Bennis, and Seong-Lyun Kim · 2020
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Rosetta: A Privacy-Preserving Framework Based on TensorFlow
Yuanfeng Chen, Gaofeng Huang, Junjie Shi, Xiang Xie, and Yilin Yan · 2020
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Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr · 2020
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Flower: A friendly federated learning research framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane · 2020
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Federated learning for healthcare informatics
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Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
Cited alongside, same era.
David Byrd and Antigoni Polychroniadou · 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.
Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 2020
Cited alongside, same era.
Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, and Yasaman Khazaeni · 2020
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.
Personalized federated learning with moreau envelopes
Canh T. Dinh, Nguyen H. Tran, and Tuan Dung Nguyen · 2020
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.
Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter B. Walker, Jiang Bian, and Fei Wang · 2021
Closest in time.
Cross-node federated graph neural network for spatio-temporal data modeling
Chuizheng Meng, Sirisha Rambhatla, and Yan Liu · 2021
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Fedmatch implementation in tensorflow
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang · 2021
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Fedprox implementation in tensorflow
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2021
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Fedbn: Federated learning on non-iid features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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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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Fast federated learning in the presence of arbitrary device unavailability
Xinran Gu, Kaixuan Huang, Jingzhao Zhang, and Longbo Huang · 2021
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Differentially private federated bayesian optimization with distributed exploration
Zhongxiang Dai, Bryan Kian Hsiang Low, and Patrick Jaillet · 2021
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Personalized federated learning with first order model optimization
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M. Alvarez · 2021
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Federated learning on non-iid data silos: An experimental study
Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He · 2021
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FedBABU: Toward enhanced representation for federated image classification
Jaehoon Oh, SangMook Kim, and Se-Young Yun · 2022
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