Active Federated Learning

Federated learning: Challenges, methods, and future directions

Original

Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2019) · 1908

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Learning from imbalanced data

He, H. and Garcia, E. A. (2008) · 2008

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Active learning literature survey

Settles, B. (2009) · 2009

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The algorithmic foundations of differential privacy

Dwork, C., Roth, A., et al. (2014) · 2014

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Federated learning: Strategies for improving communication efficiency

Original

Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D. (2016) · 2016

Earlier work this paper cites.

Communication-efficient learning of deep networks from decentralized data

Original

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al. (2016) · 2016

Cited alongside, same era.

How to backdoor federated learning

Original

Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V. (2018) · 2018

Cited alongside, same era.

Improved classification rates under refined margin conditions

Blaschzyk, I., Steinwart, I., et al. (2018) · 2018

Cited alongside, same era.

Expanding the reach of federated learning by reducing client resource requirements

Original

Caldas, S., Konečny, J., McMahan, H. B., and Talwalkar, A. (2018) · 2018

Cited alongside, same era.

Federated learning

Hartmann, F. (2018) · 2018

Cited alongside, same era.

Reddit Comments Dumps

Baumgartner, J. (2019) · 2019

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Federated learning for keyword spotting

Leroy, D., Coucke, A., Lavril, T., Gisselbrecht, T., and Dureau, J. (2019) · 2019

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Client selection for federated learning with heterogeneous resources in mobile edge

Nishio, T. and Yonetani, R. (2019) · 2019

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Federated machine learning: Concept and applications

Yang, Q., Liu, Y., Chen, T., and Tong, Y. (2019) · 2019

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