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We consider strongly convex-concave minimax problems in the federated setting, where the communication constraint is the main bottleneck.
Monotone operators associated with saddle-functions and minimax problems
Rockafellar, R. T · 1970
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On linear convergence of iterative methods for the variational inequality problem
Tseng, P · 1995
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Prox-method with rate of convergence o (1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems
Nemirovski, A · 2004
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Minibatch vs local sgd for heterogeneous distributed learning
Woodworth, B., Patel, K. K., and Srebro, N · 2006
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Mime: Mimicking centralized stochastic algorithms in federated learning
Karimireddy, S. P., Jaggi, M., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T · 2008
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Robust optimization
Ben-Tal, A., El Ghaoui, L., and Nemirovski, A · 2009
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Accelerating stochastic gradient descent using predictive variance reduction
Johnson, R. and Zhang, T · 2013
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Convex optimization: Algorithms and complexity
Bubeck, S · 2014
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Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Proximal algorithms
Parikh, N. and Boyd, S · 2014
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A universal catalyst for first-order optimization
Lin, H., Mairal, J., and Harchaoui, Z · 2015
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Stochastic variance reduction methods for saddle-point problems
Balamurugan, P. and Bach, F · 2016
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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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Local sgd converges fast and communicates little
Stich, S. U · 2018
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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
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Local sgd for saddle-point problems
Beznosikov, A., Samokhin, V., and Gasnikov, A · 2020
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Local sgd: Unified theory and new efficient methods
Gorbunov, E., Hanzely, F., and Richtárik, P · 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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A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach
Mokhtari, A., Ozdaglar, A., and Pattathil, S · 2020
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Augenstein, S., McMahan, H. B., Ramage, D., Ramaswamy, S., Kairouz, P., Chen, M., Mathews, R., et al · 2019
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Variance reduction for matrix games
Carmon, Y., Jin, Y., Sidford, A., and Tian, K · 2019
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Linear convergence of the primal-dual gradient method for convex-concave saddle point problems without strong convexity
Du, S. S. and Hu, W · 2019
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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
Cited in the paper.
Is local sgd better than minibatch sgd?
Woodworth, B., Patel, K. K., Stich, S., Dai, Z., Bullins, B., Mcmahan, B., Shamir, O., and Srebro, N
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A catalyst framework for minimax optimization
Yang, J., Zhang, S., Kiyavash, N., and He, N · 2020
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Federated accelerated stochastic gradient descent
Yuan, H. and Ma, T · 2020
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On lower iteration complexity bounds for the saddle point problems
Zhang, J., Hong, M., and Zhang, S · 2020
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