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This work addresses the problem of optimizing communications between server and clients in federated learning (FL).
Sattler, F., Müller, K.-R., and Samek, W · 1910
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Hierarchical grouping to optimize an objective function
Ward, J. H · 1963
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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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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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J · 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
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Local SGD with periodic averaging: Tighter analysis and adaptive synchronization
Haddadpour, F., Kamani, M. M., Mahdavi, M., and Cadambe, V. R · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Harry Hsu, T. M., Qi, H., and Brown, M · 2019
Cited alongside, same era.
Client selection for federated learning with heterogeneous resources in mobile edge
Nishio, T. and Yonetani, R · 2019
Cited alongside, same era.
Local SGD converges fast and communicates little
Stich, S. U · 2019
Cited alongside, same era.
Matcha: Speeding up decentralized sgd via matching decomposition sampling
Wang, J., Sahu, A. K., Yang, Z., Joshi, G., and Kar, S · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Optimal Client Sampling for Federated Learning
Chen, W., Horvath, S., and Richtarik, P · 2020
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 Richtarik, P · 2020
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On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z · 2020
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Don’t use large mini-batches, use local sgd
Lin, T., Stich, S. U., Patel, K. K., and Jaggi, M · 2020
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Tackling the objective inconsistency problem in heterogeneous federated optimization
Wang, J., Liu, Q., Liang, H., Joshi, G., and Poor, H. V · 2020
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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 · 2020
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Cited alongside, same era.
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