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In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
The Hungarian method for the assignment problem
Kuhn, H. W · 1955
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Least squares quantization in PCM
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Multilayer feedforward networks are universal approximators
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Ensemble methods in machine learning
Dietterich, T. G · 2000
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Random forests
Breiman, L · 2001
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Infinite latent feature models and the Indian buffet process
Ghahramani, Z. and Griffiths, T. L · 2005
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Stick-breaking construction for the Indian buffet process
Teh, Y. W., Grür, D., and Ghahramani, Z · 2007
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Hierarchical Beta processes and the Indian buffet process
Thibaux, R. and Jordan, M. I · 2007
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The Indian buffet process: An introduction and review
Griffiths, T. L. and Ghahramani, Z · 2011
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Large scale distributed deep networks
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Trading computation for communication: Distributed stochastic dual coordinate ascent
Yang, T · 2013
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Information-theoretic lower bounds for distributed statistical estimation with communication constraints
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Scaling distributed machine learning with the parameter server
Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., Long, J., Shekita, E. J., and Su, B.-Y · 2014
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Communication-efficient distributed optimization using an approximate newton-type method
Shamir, O., Srebro, N., and Zhang, T · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Gpu-accelerated hungarian algorithms for the linear assignment problem
Date, K. and Nagi, R · 2016
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Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)
EU · 2016
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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