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Federated optimization, an emerging paradigm which finds wide real-world applications such as federated learning, enables multiple clients (e.g., edge devices) to collaboratively optimize a global function.
Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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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 · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
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Random gradient-free minimization of convex functions
Yurii E. Nesterov and Vladimir G. Spokoiny · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Tight analyses for non-smooth stochastic gradient descent
Nicholas JA Harvey, Christopher Liaw, Yaniv Plan, and Sikander Randhawa · 2019
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Feddane: A federated newton-type method
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2019
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SlowMo: Improving communication-efficient distributed SGD with slow momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, and Michael G. Rabbat · 2020
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Federated accelerated stochastic gradient descent
Honglin Yuan and Tengyu Ma · 2020
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A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
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Adaptive federated optimization
Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. 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, Zaïd 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, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, 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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Federated learning via posterior averaging: A new perspective and practical algorithms
Maruan Al-Shedivat, Jennifer Gillenwater, Eric P. Xing, and Afshin Rostamizadeh · 2021
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Communication-efficient stochastic zeroth-order optimization for federated learning
Wenzhi Fang, Ziyi Yu, Yuning Jiang, Yuanming Shi, Colin N. Jones, and Yong Zhou · 2022
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A theoretical and empirical comparison of gradient approximations in derivative-free optimization
Albert S. Berahas, Liyuan Cao, Krzysztof Choromanski, and Katya Scheinberg · 2022
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Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
Cited alongside, same era.
On the convergence of prior-guided zeroth-order optimization algorithms
Shuyu Cheng, Guoqiang Wu, and Jun Zhu · 2021
Cited alongside, same era.
Optimizing black-box metrics with iterative example weighting
Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, and Sanmi Koyejo · 2021
Cited alongside, same era.
MetricOpt: Learning to optimize black-box evaluation metrics
Chen Huang, Shuangfei Zhai, Pengsheng Guo, and Josh M. Susskind · 2021
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas
Cited in the paper.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas
Cited in the paper.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith
Cited in the paper.
Later among the works it cites.
Sample-then-optimize batch neural Thompson sampling
Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, and Patrick Jaillet · 2022
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Accelerated federated learning with decoupled adaptive optimization
Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, and Dejing Dou · 2022
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Zeroth-order optimization with trajectory-informed derivative estimation
Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, and Bryan Kian Hsiang Low · 2023
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High probability convergence of stochastic gradient methods
Zijian Liu, Ta Duy Nguyen, Thien Hang Nguyen, Alina Ene, and Huy Lê Nguyen · 2023
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Federated neural bandit
Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, and Patrick Jaillet · 2023
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