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Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years.
Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller · 2007
Earlier work this paper cites.
Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao · 2016
Earlier work this paper cites.
The megaface benchmark: 1 million faces for recognition at scale
Ira Kemelmacher-Shlizerman, Steven M Seitz, Daniel Miller, and Evan Brossard · 2016
Earlier work this paper cites.
Frontal to profile face verification in the wild
S. Sengupta, J.-C. Chen, C. Castillo, V. M. Patel, R. Chellappa, and D.W. Jacobs · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Agedb: the first manually collected, in-the-wild age database
Stylianos Moschoglou, Athanasios Papaioannou, Christos Sagonas, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou · 2017
Earlier work this paper cites.
Iarpa janus benchmark-b face dataset
Cameron Whitelam, Emma Taborsky, Austin Blanton, Brianna Maze, Jocelyn Adams, Tim Miller, Nathan Kalka, Anil K Jain, James A Duncan, Kristen Allen, et al · 2017
Earlier work this paper cites.
Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments
Tianyue Zheng, Weihong Deng, and Jiani Hu · 2017
Earlier work this paper cites.
Vggface2: A dataset for recognising faces across pose and age
Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, and Andrew Zisserman · 2018
Earlier work this paper cites.
Iarpa janus benchmark-c: Face dataset and protocol
Brianna Maze, Jocelyn Adams, James A Duncan, Nathan Kalka, Tim Miller, Charles Otto, Anil K Jain, W Tyler Niggel, Janet Anderson, Jordan Cheney, et al · 2018
Cited alongside, same era.
Cosface: Large margin cosine loss for deep face recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu · 2018
Cited alongside, same era.
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Cited alongside, same era.
Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments
T. Zheng and W. Deng · 2018
Cited alongside, same era.
Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation
Yang Chen, Xiaoyan Sun, and Yaochu Jin · 2019
Cited alongside, same era.
SCAFFOLD: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Later among the works it cites.
Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
Later among the works it cites.
A unified theory of decentralized SGD with changing topology and local updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, and Sebastian U. Stich · 2020
Later among the works it cites.
Slowmo: Improving communication-efficient distributed SGD with slow momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, and Michael G. Rabbat · 2020
Later among the works it cites.
Is local SGD better than minibatch SGD?
Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, and Nathan Srebro · 2020
Later among the works it cites.
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Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
Cited alongside, same era.
Local SGD converges fast and communicates little
Sebastian U. Stich · 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.
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
Cited alongside, same era.
Hongda Wu and Ping Wang · 2020
Later among the works it cites.
Federated meta-learning for fraudulent credit card detection
Wenbo Zheng, Lan Yan, Chao Gou, and Fei-Yue Wang · 2020
Later among the works it cites.
Performance optimization of federated person re-identification via benchmark analysis
Weiming Zhuang, Yonggang Wen, Xuesen Zhang, Xin Gan, Daiying Yin, Dongzhan Zhou, Shuai Zhang, and Shuai Yi · 2020
Later among the works it cites.