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Federated Learning (FL) has become a popular paradigm for learning from distributed data.
Introductory lectures on convex optimization: A basic course
Y. Nesterov, · 2004
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“Distributed optimization and statistical learning via the alternating direction method of multipliers,”
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, · 2011
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“Penalized likelihood regression for generalized linear models with non-quadratic penalties,”
Anestis Antoniadis, Irène Gijbels, and Mila Nikolova, · 2011
Earlier work this paper cites.
“An empirical study of learning rates in deep neural networks for speech recognition,”
Andrew Senior, Georg Heigold, Marc’Aurelio Ranzato, and Ke Yang, · 2013
Earlier work this paper cites.
“Federated learning: Strategies for improving communication efficiency,”
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon, · 2016
Earlier work this paper cites.
“Stochastic gradient descent with finite samples sizes,”
K. Yuan, B. Ying, S. Vlaski, and A. H. Sayed, · 2016
Earlier work this paper cites.
“Federated multi-task learning,”
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar, · 2017
Earlier work this paper cites.
“Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,”
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu, · 2017
Earlier work this paper cites.
“Optimal algorithms for smooth and strongly convex distributed optimization in networks,”
K. Scaman, F. Bach, S. Bubeck, Y. Lee, and L. Massoulié, · 2017
Earlier work this paper cites.
Jianyu Wang and Gauri Joshi, · 2018
Earlier work this paper cites.
“When edge meets learning: Adaptive control for resource-constrained distributed machine learning,”
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan, · 2018
Earlier work this paper cites.
“On the convergence of federated optimization in heterogeneous networks,”
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith, · 2018
Cited alongside, same era.
“LAG: Lazily aggregated gradient for communication-efficient distributed learning,”
Tianyi Chen, Georgios Giannakis, Tao Sun, and Wotao Yin, · 2018
Cited alongside, same era.
“Leaf: A benchmark for federated settings,”
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar, · 2018
Cited alongside, same era.
“Federated learning: Challenges, methods, and future directions,”
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith, · 2019
Cited alongside, same era.
“On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization,”
“Trading redundancy for communication: Speeding up distributed SGD for non-convex optimization,”
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, and Viveck Cadambe, · 2019
Later among the works it cites.
“First analysis of local GD on heterogeneous data,”
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik, · 2019
Later among the works it cites.
“Variance reduced local SGD with lower communication complexity,”
Xianfeng Liang, Shuheng Shen, Jingchang Liu, Zhen Pan, Enhong Chen, and Yifei Cheng, · 2019
Later among the works it cites.
“Convergence of distributed stochastic variance reduced methods without sampling extra data,”
Shicong Cen, Huishuai Zhang, Yuejie Chi, Wei Chen, and Tie-Yan Liu, · 2019
Later among the works it cites.
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Hao Yu, Rong Jin, and Sen Yang, · 2019
Cited alongside, same era.
“Local sgd converges fast and communicates little,”
Sebastian Urban Stich, · 2019
Cited alongside, same era.
“In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning,”
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, · 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 Konecny, Stefano Mazzocchi, H Brendan McMahan, et al., · 2019
Cited alongside, same era.
“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.
“Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning,”
Hao Yu, Sen Yang, and Shenghuo Zhu, · 2019
Cited alongside, same era.
Pranay Sharma, Prashant Khanduri, Saikiran Bulusu, Ketan Rajawat, and Pramod K Varshney, · 2019
Later among the works it cites.
“Lower bounds for finding stationary points i,”
Y. Carmon, J. C. Duchi, O. Hinder, and A. Sidford, · 2019
Later among the works it cites.
“Distributed non-convex first-order optimization and information processing: Lower complexity bounds and rate optimal algorithms,”
H. Sun and M. Hong, · 2019
Later among the works it cites.
“COLA: Communication-censored linearized admm for decentralized consensus optimization,”
W. Li, Y. Liu, Z. Tian, and Q. Ling, · 2019
Later among the works it cites.
“iDLG: Improved deep leakage from gradients,” 2020
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen, · 2020
Closest in time.
“FedDANE: A federated newton-type method,”
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith, · 2020
Closest in time.