Fetching the paper…
Reading the bibliography…
DNN-based face recognition models require large centrally aggregated face datasets for training.
Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
Earlier work this paper cites.
Learning face representation from scratch
D. Yi, Z. Lei, S. Liao, and S. Z. Li · 2014
Earlier work this paper cites.
Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark A
B. F. Klare, B. Klein, E. Taborsky, A. Blanton, J. Cheney, K. Allen, P. Grother, A. Mah, and A. K. Jain · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y Arcas · 2017
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage · 2018
Earlier work this paper cites.
Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2018
Earlier work this paper cites.
Iarpa janus benchmark-C: Face dataset and protocol
B. Maze, J. Adams, J. A. Duncan, N. Kalka, T. Miller, C. Otto, A. K. Jain, W. T. Niggel, J. Anderson, J. Cheney, et al · 2018
Cited alongside, same era.
Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu · 2018
Cited alongside, same era.
Applied federated learning: Improving google keyboard query suggestions
T. Yang, G. Andrew, H. Eichner, H. Sun, W. Li, N. Kong, D. Ramage, and F. Beaufays · 2018
Cited alongside, same era.
Generative models for effective ML on private, decentralized datasets
S. Augenstein, H. B. McMahan, D. Ramage, S. Ramaswamy, P. Kairouz, M. Chen, R. Mathews, et al · 2019
Cited alongside, same era.
Towards federated learning at scale: System design
K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečnỳ, S. Mazzocchi, H. B. McMahan, et al · 2019
Federated learning for emoji prediction in a mobile keyboard
S. Ramaswamy, R. Mathews, K. Rao, and F. Beaufays · 2019
Later among the works it cites.
https://bit.ly/2Ps1gzX
NIST FRVT report 2020 · 2020
Later among the works it cites.
Loadaboost: Loss-based adaboost federated machine learning with reduced computational complexity on iid and non-iid intensive care data
L. Huang, Y. Yin, Z. Fu, S. Zhang, H. Deng, and D. Liu · 2020
Later among the works it cites.
Federated learning: Challenges, methods, and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 2020
Later among the works it cites.
R. Shao, P. Perera, P. C. Yuen, and V. M. Patel · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Federated learning of out-of-vocabulary words
M. Chen, R. Mathews, T. Ouyang, and F. Beaufays · 2019
Cited alongside, same era.
Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al · 2019
Cited alongside, same era.
Agnostic federated learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
Cited alongside, same era.
https://exposing.ai/brainwash/
Brainwash Dataset
Cited in the paper.
https://bit.ly/2R8aD8j
California consumer privacy act
Cited in the paper.
https://lat.ms/3uqRn3Z
Clearview ai uses your online photos to instantly id you. that’s a problem, lawsuit says
Cited in the paper.
https://exposing.ai/duke_mtmc/
Duke MTMC Dataset
Cited in the paper.
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, M. Milchenko, W. Xu, D. Marcus, R. R. Colen, et al · 2020
Later among the works it cites.
Federated learning with differential privacy: Algorithms and performance analysis
K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. Quek, and H. V. Poor · 2020
Later among the works it cites.
Federated learning with only positive labels
F. Yu, A. S. Rawat, A. Menon, and S. Kumar · 2020
Later among the works it cites.