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Federated Learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local.
Distributed learning with compressed gradient differences
Mishchenko, K., Gorbunov, E., Takáč, M., and Richtárik, P. (2019) · 1901
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Asynchronous federated optimization
Xie, C., Koyejo, S., and Gupta, I. (2019) · 1903
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Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2019a) · 1908
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Stich, S. U. and Karimireddy, S. P. (2019) · 1909
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On the convergence of local descent methods in federated learning
Haddadpour, F. and Mahdavi, M. (2019) · 1910
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Scaffold: Stochastic controlled averaging for on-device federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T. (2019) · 1910
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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) · 1912
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Variance reduced local sgd with lower communication complexity
Liang, X., Shen, S., Liu, J., Pan, Z., Chen, E., and Cheng, Y. (2019) · 1912
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Concentration inequalities and empirical processes theory applied to the analysis of learning algorithms
Bousquet, O. (2002) · 2002
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Faster on-device training using new federated momentum algorithm
Huo, Z., Yang, Q., Gu, B., Huang, L. C., et al. (2020) · 2002
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A unified theory of decentralized sgd with changing topology and local updates
Koloskova, A., Loizou, N., Boreiri, S., Jaggi, M., and Stich, S. U. (2020) · 2003
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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) · 2003
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From local sgd to local fixed point methods for federated learning
Malinovsky, G., Kovalev, D., Gasanov, E., Condat, L., and Richtarik, P. (2020) · 2004
Cited alongside, same era.
Fedsplit: An algorithmic framework for fast federated optimization
Pathak, R. and Wainwright, M. J. (2020) · 2005
Cited alongside, same era.
Convexity, classification, and risk bounds
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D. (2006) · 2006
Cited alongside, same era.
Federated learning with compression: Unified analysis and sharp guarantees
Haddadpour, F., Kamani, M. M., Mokhtari, A., and Mahdavi, M. (2020) · 2007
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Large scale empirical risk minimization via truncated adaptive Newton method
Eisen, M., Mokhtari, A., and Ribeiro, A. (2018) · 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) · 2018
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Local sgd with periodic averaging: Tighter analysis and adaptive synchronization
Haddadpour, F., Kamani, M. M., Mahdavi, M., and Cadambe, V. (2019) · 2019
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Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T. (2019) · 2019
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Efficient nonconvex empirical risk minimization via adaptive sample size methods
Mokhtari, A., Ozdaglar, A., and Jadbabaie, A. (2019) · 2019
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Wang, J., Liu, Q., Liang, H., Joshi, G., and Poor, H. V. (2020) · 2007
Cited alongside, same era.
The nature of statistical learning theory
Vapnik, V. (2013) · 2013
Cited alongside, same era.
Competing with the empirical risk minimizer in a single pass
Frostig, R., Ge, R., Kakade, S. M., and Sidford, A. (2015) · 2015
Cited alongside, same era.
Adaptive Newton method for empirical risk minimization to statistical accuracy
Mokhtari, A., Daneshmand, H., Lucchi, A., Hofmann, T., and Ribeiro, A. (2016) · 2016
Cited alongside, same era.
Speeding up distributed machine learning using codes
Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., and Ramchandran, K. (2017) · 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) · 2017
Cited alongside, same era.
First-order adaptive sample size methods to reduce complexity of empirical risk minimization
Mokhtari, A. and Ribeiro, A. (2017) · 2017
Cited alongside, same era.
Zhou, F. and Cong, G. (2017) · 2017
Cited alongside, same era.
Nishio, T. and Yonetani, R. (2019) · 2019
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Local sgd converges fast and communicates little
Stich, S. U. (2019) · 2019
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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) · 2019
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Tighter theory for local sgd on identical and heterogeneous data
Bayoumi, A. K. R., Mishchenko, K., and Richtarik, P. (2020) · 2020
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Efficient distributed hessian free algorithm for large-scale empirical risk minimization via accumulating sample strategy
Jahani, M., He, X., Ma, C., Mokhtari, A., Mudigere, D., Ribeiro, A., and Takác, M. (2020) · 2020
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., and Pedarsani, R. (2020b) · 2031
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