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Federated learning (FL), which utilizes communication between the server (core) and local devices (edges) to indirectly learn from more data, is an emerging field in deep learning research.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images.(2009), 2009
Krizhevsky, A., Hinton, G., et al · 2009
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Communication efficient distributed machine learning with the parameter server
Li, M., Andersen, D. G., Smola, A. J., and Yu, K · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 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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Gradient coding: Avoiding stragglers in distributed learning
Tandon, R., Lei, Q., Dimakis, A. G., and Karampatziakis, N · 2017
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Tarvainen, A. and Valpola, H · 2017
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Paraphrasing complex network: Network compression via factor transfer
Kim, J., Park, S., and Kwak, N · 2018
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Interpretable convolutional neural networks
Zhang, Q., Wu, Y. N., and Zhu, S.-C · 2018
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks, 2019
Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, T. N., and Khazaeni, Y · 2019
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Feddistill: Making bayesian model ensemble applicable to federated learning
Chen, H.-Y. and Chao, W.-L · 2020
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Federated optimization in heterogeneous networks, 2020
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2020
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Ensemble distillation for robust model fusion in federated learning
Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
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Ibm federated learning: an enterprise framework white paper v0. 1
Ludwig, H., Baracaldo, N., Thomas, G., Zhou, Y., Anwar, A., Rajamoni, S., Ong, Y., Radhakrishnan, J., Verma, A., Sinn, M., et al · 2020
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Chen, Y., Sun, X., and Jin, Y · 2019
Cited alongside, same era.
Guha, N., Talwalkar, A., and Smith, V · 2019
Cited alongside, same era.
Knockoff nets: Stealing functionality of black-box models
Orekondy, T., Schiele, B., and Fritz, M · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications, 2019
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
Cited alongside, same era.
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Federated knowledge distillation
Seo, H., Park, J., Oh, S., Bennis, M., and Kim, S.-L · 2020
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Feded: Federated learning via ensemble distillation for medical relation extraction
Sui, D., Chen, Y., Zhao, J., Jia, Y., Xie, Y., and Sun, W · 2020
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Federated learning with matched averaging
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y · 2020
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