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Federated learning is a machine learning technique that enables training across decentralized data.
On the convergence of FedAvg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 1907
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 1908
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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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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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Distributed mean estimation with limited communication
Ananda Theertha Suresh, Felix X Yu, Sanjiv Kumar, and H Brendan McMahan · 2017
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cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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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 learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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On the convergence of federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 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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Tensorflow federated, 2018
TFF · 2018
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Applied federated learning: Improving google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
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On the convergence of local descent methods in federated learning
Farzin Haddadpour and Mehrdad Mahdavi · 2019
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for on-device federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2019
Cited alongside, same era.
Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Cited alongside, same era.
Private federated learning with domain adaptation
Daniel Peterson, Pallika Kanani, and Virendra J Marathe · 2019
Cited alongside, same era.
Haiku: Sonnet for JAX, 2020
Tom Hennigan, Trevor Cai, Tamara Norman, and Igor Babuschkin · 2020
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Optax: composable gradient transformation and optimisation, in jax!, 2020
Matteo Hessel, David Budden, Fabio Viola, Mihaela Rosca, Eren Sezener, and Tom Hennigan · 2020
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Fairness and accuracy in federated learning, 2020
Wei Huang, Tianrui Li, Dexian Wang, Shengdong Du, and Junbo Zhang · 2020
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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 · 2020
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Tighter theory for local SGD on indentical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
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Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Cited alongside, same era.
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
Cited alongside, same era.
Mitigating bias in federated learning, 2020
Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, and Heiko Ludwig · 2020
Cited alongside, same era.
Fairness-aware agnostic federated learning, 2020
Wei Du, Depeng Xu, Xintao Wu, and Hanghang Tong · 2020
Cited alongside, same era.
Jraph: A library for graph neural networks in jax., 2020
Jonathan Godwin*, Thomas Keck*, Peter Battaglia, Victor Bapst, Thomas Kipf, Yujia Li, Kimberly Stachenfeld, Petar Veličković, and Alvaro Sanchez-Gonzalez · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Flax: A neural network library and ecosystem for JAX, 2020
Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2020
Cited alongside, same era.
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2020
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Ibm federated learning: an enterprise framework white paper v0. 1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, et al · 2020
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Objax, 2020
Objax Developers · 2020
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Adaptive federated optimization, 2020
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H. Brendan McMahan · 2020
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Ternary compression for communication-efficient federated learning
Jinjin Xu, Wenli Du, Yaochu Jin, Wangli He, and Ran Cheng · 2020
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Advances and open problems in federated learning, 2021
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2021
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Communication-efficient agnostic federated averaging
Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, and Ananda Theertha Suresh · 2021
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