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We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d.
Online convex programming and generalized infinitesimal gradient ascent
Zinkevich, M · 2003
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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 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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The General Data Protection Regulation (EU) 2016/679 (GDPR)
European Parliament and Council of the European Union · 2018
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Asynchronous decentralized parallel stochastic gradient descent
Lian, X., Zhang, W., Zhang, C., and Liu, J · 2018
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Sparsified SGD with memory
Stich, S. U., Cordonnier, J., and Jaggi, M · 2018
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Semi-cyclic stochastic gradient descent
Eichner, H., Koren, T., McMahan, B., Srebro, N., and Talwar, K · 2019
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Making ai forget you: Data deletion in machine learning
Ginart, A. A., Guan, M., Valiant, G., and Zou, J · 2019
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The non-iid data quagmire of decentralized machine learning
Hsieh, K., Phanishayee, A., Mutlu, O., and Gibbons, P. B · 2019
Cited alongside, same era.
Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konecný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Raykova, M., Qi, H., Ramage, D., Raskar, R., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S · 2019
Cited alongside, same era.
Communication compression for decentralized training
Tang, H., Gan, S., Zhang, C., Zhang, T., and Liu, J
Cited in the paper.
D 2 {D}^{2} : Decentralized training over decentralized data
Tang, H., Lian, X., Yan, M., Zhang, C., and Liu, J
Cited in the paper.
On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization
Yu, H., Jin, R., and Yang, S
Cited in the paper.
Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning
Yu, H., Yang, S., and Zhu, S
Cited in the paper.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2019
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Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T · 2019
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Local SGD converges fast and communicates little
Stich, S. U · 2019
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DoubleSqueeze
Tang, H., Yu, C., Lian, X., Zhang, T., and Liu, J · 2019
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On the computation and communication complexity of parallel SGD with dynamic batch sizes for stochastic non-convex optimization
Yu, H. and Jin, R · 2019
Later among the works it cites.
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