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Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a central location.
Adaptive mixtures of local experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E · 1991
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Hierarchical mixtures of experts and the em algorithm
Jordan, M. I. and Jacobs, R. A · 1994
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, A. et al · 2009
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Mnist handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Group normalization
Wu, Y. and He, K · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al · 2016
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Federated meta-learning with fast convergence and efficient communication
Chen, F., Luo, M., Dong, Z., Li, Z., and He, X · 2018
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Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
Earlier work this paper cites.
Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
Cited alongside, same era.
Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecny, J., Mazzocchi, S., McMahan, H. B., et al · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H., and Brown, M · 2019
Cited alongside, same era.
Improving federated learning personalization via model agnostic meta learning
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J., Ren, J., and Nado, Z · 2019
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Federated learning with hierarchical clustering of local updates to improve training on non-iid data
Briggs, C., Fan, Z., and Andras, P · 2020
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Adaptive personalized federated learning
Deng, Y., Kamani, M. M., and Mahdavi, M · 2020
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Efficient and scalable bayesian neural nets with rank-1 factors
Dusenberry, M., Jerfel, G., Wen, Y., Ma, Y., Snoek, J., Heller, K., Lakshminarayanan, B., and Tran, D · 2020
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Jiang, Y., Konečnỳ, J., Rush, K., and Kannan, S · 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., et al · 2019
Cited alongside, same era.
Adaptive gradient-based meta-learning methods
Khodak, M., Balcan, M.-F. F., and Talwalkar, A. S · 2019
Cited alongside, same era.
Fair resource allocation in federated learning
Li, T., Sanjabi, M., and Smith, V · 2019
Cited alongside, same era.
Mohri, M., Sivek, G., and Suresh, A. T · 2019
Cited alongside, same era.
Sattler, F., Müller, K.-R., and Samek, W · 2019
Cited alongside, same era.
Expanding the reach of federated learning by reducing client resource requirements
Caldas, S., Konečny, J., McMahan, H. B., and Talwalkar, A
Cited in the paper.
Leaf: A benchmark for federated settings
Caldas, S., Wu, P., Li, T., Konečnỳ, J., McMahan, H. B., Smith, V., and Talwalkar, A
Cited in the paper.
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
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On federated learning of deep networks from non-{iid} data: Parameter divergence and the effects of hyperparametric methods, 2020
Kim, H., Kim, T., and Youn, C.-H · 2020
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Survey of personalization techniques for federated learning
Kulkarni, V., Kulkarni, M., and Pant, A · 2020
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Think locally, act globally: Federated learning with local and global representations
Liang, P. P., Liu, T., Ziyin, L., Salakhutdinov, R., and Morency, L.-P · 2020
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Three approaches for personalization with applications to federated learning
Mansour, Y., Mohri, M., Ro, J., and Suresh, A. T · 2020
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Adaptive federated optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečnỳ, J., Kumar, S., and McMahan, H. B · 2020
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