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Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data.
Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
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Exponential bounds for multivariate self-normalized sums
Patrice Bertail, Emmanuelle Gautherat, and Hugo Harari-Kermadec · 2008
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Introduction to probability and statistics
William Mendenhall, Robert J Beaver, and Barbara M Beaver · 2012
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Introduction to probability and statistics
William Mendenhall, Robert J Beaver, and Barbara M Beaver · 2012
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Ulfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Concentration inequalities for sampling without replacement
Rémi Bardenet and Odalric-Ambrym Maillard · 2015
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Low latency Geo-distributed data analytics
Qifan Pu, Ganesh Ananthanarayanan, Peter Bodik, Srikanth Kandula, Aditya Akella, Victor Bahl, and Ion Stoica · 2015
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Global analytics in the face of bandwidth and regulatory constraints
Ashish Vulimiri, Carlo Curino, B Godfrey, J Padhye, and G Varghese · 2015
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Stochastic optimization with importance sampling for regularized loss minimization
Peilin Zhao and Tong Zhang · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Adaptive sampling for SGD by exploiting side information
Siddharth Gopal · 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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Via: Improving internet telephony call quality using predictive relay selection
Junchen Jiang, Rajdeep Das, Ganesh Ananthanarayanan, Philip A Chou, Venkata Padmanabhan, Vyas Sekar, Esbjorn Dominique, Marcin Goliszewski, Dalibor Kukoleca, Renat Vafin, et al · 2016
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Clarinet: WAN-aware optimization for analytics queries
Raajay Viswanathan, Ganesh Ananthanarayanan, and Aditya Akella · 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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Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, and et al · 2017
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Prio: Private, robust, and scalable computation of aggregate statistics
Henry Corrigan-Gibbs and Dan Boneh · 2017
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Learning with privacy at scale
Apple Differential Privacy Team · 2017
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Differentially private federated learning: A client level perspective
Robin C. Geyer, Tassilo Klein, and Moin Nabi · 2017
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Gaia: Geo-distributed machine learning approaching LAN speeds
Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, Phillip B. Gibbons, and Onur Mutlu · 2017
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Biased importance sampling for deep neural network training
Angelos Katharopoulos and Francois Fleuret · 2017
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Communication-efficient learning of deep networks from decentralized data
H. 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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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Focus: Querying large video datasets with low latency and low cost
Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik, Shivaram Venkataraman, Paramvir Bahl, Matthai Philipose, Phillip B Gibbons, and Onur Mutlu · 2018
Cited alongside, same era.
Training deep models faster with robust, approximate importance sampling
Tyler B. Johnson and Carlos Guestrin · 2018
Cited alongside, same era.
Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and Francois Fleuret · 2018
Cited alongside, same era.
To relay or not to relay for inter-cloud transfers?
Fan Lai, Mosharaf Chowdhury, and Harsha Madhyastha · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Speech commands: A dataset for limited-vocabulary speech recognition
Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD
Jianyu Wang and Gauri Joshi · 2019
Later among the works it cites.
Federated evaluation of on-device personalization
Kangkang Wang, Rajiv Mathews, Chloe Kiddon, Hubert Eichner, Francoise Beaufays, and Daniel Ramage · 2019
Later among the works it cites.
A first look at deep learning apps on smartphones
Mengwei Xu, Jiawei Liu, Yuanqiang Liu, Felix Xiaozhu Lin, Yunxin Liu, and Xuanzhe Liu · 2019
Later among the works it cites.
Generative models for effective ML on private, decentralized datasets
Sean Augenstein, H Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, et al · 2020
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Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
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Pete Warden · 2018
Cited alongside, same era.
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
Cited alongside, same era.
AWStream: Adaptive wide-area streaming analytics
Ben Zhang, Xin Jin, Sylvia Ratnasamy, John Wawrzynek, and Edward A. Lee · 2018
Cited alongside, same era.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
Cited alongside, same era.
https://www.nytimes.com/2019/05/07/opinion/google-sundar-pichai-privacy.html
Google’s Sundar Pichai: Privacy Should Not Be a Luxury Good · 2019
Cited alongside, same era.
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, H Brendan McMahan, et al · 2019
Cited alongside, same era.
Data validation for machine learning
Eric Breck, Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich · 2019
Cited alongside, same era.
Closest in time.
Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing
S. Dutta, D. Wei, H. Yueksel, P. Y. Chen, S. Liu, and K. R. Varshney · 2020
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Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2020
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The Non-IID data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B. Gibbons · 2020
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Federated visual classification with real-world data distribution
Harry Hsu, Hang Qi, and Matthew Brown · 2020
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Sol: Fast distributed computation over slow networks
Fan Lai, Jie You, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury · 2020
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ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Fair resource allocation in federated learning
Tian Li, Manzil Zaheer, Ahmad Beirami, and Virginia Smith · 2020
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Don’t use large mini-batches, use local SGD
Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, and Martin Jaggi · 2020
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Themis: Fair and efficient GPU cluster scheduling
Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, and Shuchi Chawla · 2020
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Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Suresh · 2020
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Federated adversarial domain adaptation
Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko · 2020
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Training production language models without memorizing user data
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, and Françoise Beaufays · 2020
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Understanding and benchmarking the impact of GDPR on database systems
Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, and Vijay Chidambaram · 2020
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Near-optimal latency versus cost tradeoffs in geo-distributed storage
Muhammed Uluyol, Anthony Huang, Ayush Goel, Mosharaf Chowdhury, and Harsha V. Madhyastha · 2020
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Learning in situ: a randomized experiment in video streaming
Francis Y Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein · 2020
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Federated learning with only positive labels
Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, and Sanjiv Kumar · 2020
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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FedScale: Benchmarking model and system performance of federated learning
Fan Lai, Yinwei Dai, Xiangfeng Zhu, and Mosharaf Chowdhury · 2021
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2021
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