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Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices.
Structured second-and higher-order derivatives through univariate taylor series
Bischof, C., Corliss, G., and Griewank, A · 1993
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
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The elements of statistical learning , volume 1
Friedman, J., Hastie, T., and Tibshirani, R · 2001
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Higher-order derivatives and taylor’s formula in several variables
Folland, G · 2005
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A simple peer-to-peer algorithm for distributed optimization in sensor networks
Johansson, B., Rabi, M., and Johansson, M · 2007
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Twitter sentiment classification using distant supervision
Go, A., Bhayani, R., and Huang, L · 2009
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Parallelized stochastic gradient descent
Zinkevich, M., Weimer, M., Li, L., and Smola, A. J · 2010
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Distributed random projection algorithm for convex optimization
Lee, S. and Nedic, A · 2013
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Understanding machine learning: From theory to algorithms
Shalev-Shwartz, S. and Ben-David, S · 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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Recurrent neural network regularization
Zaremba, W., Sutskever, I., and Vinyals, O · 2014
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The loss surfaces of multilayer networks
Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., and LeCun, Y · 2015
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Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
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Deep learning without poor local minima
Kawaguchi, K · 2016
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Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
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Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
Analyzing federated learning through an adversarial lens
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S · 2019
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Local model poisoning attacks to byzantine-robust federated learning
Fang, M., Cao, X., Jia, J., and Gong, N. Z · 2019
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On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z · 2019
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Robust and communication-efficient federated learning from non-iid data
Sattler, F., Wiedemann, S., Müller, K.-R., and Samek, W · 2019
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Stich, S. U. and Karimireddy, S. P · 2019
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Zhou, F. and Cong, G · 2017
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
Cited alongside, same era.
Federated optimization for heterogeneous networks
Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Local sgd converges fast and communicates little
Stich, S. U · 2018
Cited alongside, same era.
Sparsified sgd with memory
Stich, S. U., Cordonnier, J.-B., and Jaggi, M · 2018
Cited alongside, same era.
Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Woodworth, B. E., Wang, J., Smith, A., McMahan, B., and Srebro, N · 2018
Cited alongside, same era.
Applied federated learning: Improving google keyboard query suggestions
Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., and Beaufays, F · 2018
Cited alongside, same era.
Adaptive federated learning in resource constrained edge computing systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., and Chan, K · 2019
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Yu, H., Yang, S., and Zhu, S · 2019
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A tight convergence analysis for stochastic gradient descent with delayed updates
Arjevani, Y., Shamir, O., and Srebro, N · 2020
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Asynchronous distributed optimization with randomized delays
Glasgow, M. and Wootters, M · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A. T · 2020
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Tighter theory for local sgd on identical and heterogeneous data
Khaled, A., Mishchenko, K., and Richtárik, P · 2020
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Federated learning’s blessing: Fedavg has linear speedup
Qu, Z., Lin, K., Kalagnanam, J., Li, Z., Zhou, J., and Zhou, Z · 2020
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Distributed non-convex optimization with sublinear speedup under intermittent client availability
Yan, Y., Niu, C., Ding, Y., Zheng, Z., Wu, F., Chen, G., Tang, S., and Wu, Z · 2020
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Achieving linear speedup with partial worker participation in non-{iid} federated learning
Yang, H., Fang, M., and Liu, J · 2021
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