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Federated learning (FL) is a distributed learning technique that trains a shared model over distributed data in a privacy-preserving manner.
Towards federated learning at scale: System design, 2019
K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, H. B. McMahan, T. Van Overveldt, D. Petrou, D. Ramage, and J. Roselander · 1902
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An Introduction to Probability Theory and Its Applications
W. Feller · 1968
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Twitter sentiment classification using distant supervision
A. Go, R. Bhayani, and L. Huang · 2009
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Performance Modeling and Design of Computer Systems: Queueing Theory in Action
M. Harchol-Balter · 2013
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Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. Manning · 2014
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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A view from the other side: Understanding mobile phone characteristics in the developing world
S. Ahmad, A. L. Haamid, Z. A. Qazi, Z. Zhou, T. Benson, and I. A. Qazi · 2016
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Performance characterization of a commercial video streaming service
M. Ghasemi, P. Kanuparthy, A. Mansy, T. Benson, and J. Rexford · 2016
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EMNIST: an extension of MNIST to handwritten letters
G. Cohen, S. Afshar, J. Tapson, and A. van Schaik · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas · 2017
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Federated multi-task learning
V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar · 2017
Cited alongside, same era.
The EU General Data Protection Regulation (GDPR): A Practical Guide
P. Voigt and A. v. d. Bussche · 2017
Cited alongside, same era.
Expanding the reach of federated learning by reducing client resource requirements
S. Caldas, J. Konečný, H. B. McMahan, and A. Talwalkar · 2018
Cited alongside, same era.
LEAF: A benchmark for federated settings
S. Caldas, P. Wu, T. Li, J. Konečný, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization, 2018
T. B. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang · 2018
Cited alongside, same era.
Heterofl: Computation and communication efficient federated learning for heterogeneous clients
E. Diao, J. Ding, and V. Tarokh · 2020
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Fedmgda+: Federated learning meets multi-objective optimization
Z. Hu, K. Shaloudegi, G. Zhang, and Y. Yu · 2020
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Ditto: Fair and robust federated learning through personalization
T. Li, S. Hu, A. Beirami, and V. Smith · 2020
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Mobile web browsing under memory pressure
I. A. Qazi, Z. A. Qazi, T. A. Benson, G. Murtaza, E. Latif, A. Manan, and A. Tariq · 2020
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Federated optimization in heterogeneous networks
A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. S. Talwalkar, and V. Smith · 2020
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The California Consumer Privacy Act (CCPA): An implementation guide
P. BUKATY · 2019
Cited alongside, same era.
Model pruning enables efficient federated learning on edge devices
Y. Jiang, S. Wang, V. Valls, B. J. Ko, W.-H. Lee, K. K. Leung, and L. Tassiulas · 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. A. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D’Oliveira, S. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett, A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He, L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konečný, A. Korolova, F. Koushanfar, S. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, M. Raykova, H. Qi, D. Ramage, R. Raskar, D. Song, W. Song, S. U. Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang, L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, and S. Zhao · 2019
Cited alongside, same era.
Fair resource allocation in federated learning
T. Li, M. Sanjabi, and V. Smith · 2019
Cited alongside, same era.
Agnostic federated learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
Cited alongside, same era.
https://tinyurl.com/33yk98s7
Understanding Android Memory Usage (Google 1/O’18)
Cited in the paper.
Firebase services
Cited in the paper.
Dropnet: Reducing neural network complexity via iterative pruning
C. M. J. Tan and M. Motani · 2020
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DropNet: Reducing neural network complexity via iterative pruning
C. M. J. Tan and M. Motani · 2020
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Webmedic: Disentangling the memory-functionality tension for the next billion mobile web users
U. Naseer, T. A. Benson, and R. Netravali · 2021
Closest in time.
A field guide to federated optimization
J. Wang, Z. Charles, Z. Xu, G. Joshi, H. B. McMahan, B. A. y Arcas, M. Al-Shedivat, G. Andrew, S. Avestimehr, K. Daly, D. Data, S. N. Diggavi, H. Eichner, A. Gadhikar, Z. Garrett, A. M. Girgis, F. Hanzely, A. Hard, C. He, S. Horvath, Z. Huo, A. Ingerman, M. Jaggi, T. Javidi, P. Kairouz, S. Kale, S. P. Karimireddy, J. Konečný, S. Koyejo, T. Li, L. Liu, M. Mohri, H. Qi, S. J. Reddi, P. Richtárik, K. Singhal, V. Smith, M. Soltanolkotabi, W. Song, A. T. Suresh, S. U. Stich, A. Talwalkar, H. Wang, B. E. Woodworth, S. Wu, F. X. Yu, H. Yuan, M. Zaheer, M. Zhang, T. Zhang, C. Zheng, C. Zhu, and W. Zhu · 2021
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Asynchronous federated learning on heterogeneous devices: A survey, 2021
C. Xu, Y. Qu, Y. Xiang, and L. Gao · 2021
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