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For the sake of protecting data privacy and due to the rapid development of mobile devices, e.g., powerful central processing unit (CPU) and nascent neural processing unit (NPU), collaborative machine learning on mobile devices, e.g., federated learning, has been envisioned as a new AI approach with broad application prospects.
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Earlier work this paper cites.
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B. Gu, F. Hu and H. Liu, · 2001
Earlier work this paper cites.
An introduction to game theory
M. J. Osborne et al., · 2004
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“Efficient large-scale graph processing on hybrid cpu and gpu systems,”
A. Gharaibeh, T. Reza, E. Santos-Neto, L. B. Costa, S. Sallinen and M. Ripeanu, · 2013
Cited alongside, same era.
“Cybersecurity, data breaches, and the economic loss doctrine in the payment card industry,”
D. W. Opderbeck, · 2015
Cited alongside, same era.
“Federated optimization: Distributed machine learning for on-device intelligence,”
J. Konecnỳ, H. B. McMahan, D. Ramage and P. Richtárik, · 2016
Cited alongside, same era.
“Federated learning: Collaborative machine learning without centralized training data,” https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
google, · 2017
Later among the works it cites.
S. Feng, W. Wang, D. Niyato, D. I. Kim and P. Wang, · 2018
Closest in time.
“Machine learning and mobile: Deploying models on the edge,” https://blog.algorithmia.com/machine-learning-and-mobile-deploying-models-on-the-edge/
TensorFlow, · 2018
Closest in time.
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