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Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabilities, which call for flexible worker participation in terms of timing, effort, data heterogeneity, etc.
Fair resource allocation in federated learning
Li, T., Sanjabi, M., Beirami, A., and Smith, V · 1905
Earlier work this paper cites.
On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z · 1907
Earlier work this paper cites.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 1908
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Fedpd: A federated learning framework with optimal rates and adaptivity to non-iid data
Zhang, X., Hong, M., Dhople, S., Yin, W., and Liu, Y · 2005
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Hogwild!: A lock-free approach to parallelizing stochastic gradient descent
Niu, F., Recht, B., Ré, C., and Wright, S. J · 2011
Earlier work this paper cites.
Distributed delayed stochastic optimization
Agarwal, A. and Duchi, J. C · 2012
Earlier work this paper cites.
A stochastic gradient method with an exponential convergence rate for finite training sets
Le Roux, N., Schmidt, M. W., and Bach, F. R · 2012
Earlier work this paper cites.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. and Lan, G · 2013
Earlier work this paper cites.
Gpu asynchronous stochastic gradient descent to speed up neural network training
Paine, T., Jin, H., Yang, J., Lin, Z., and Huang, T · 2013
Earlier work this paper cites.
Asynchronous parallel stochastic gradient for nonconvex optimization
Lian, X., Huang, Y., Li, Y., and Liu, J · 2015
Earlier work this paper cites.
Deep learning with elastic averaging sgd
Zhang, S., Choromanska, A. E., and LeCun, Y · 2015
Earlier work this paper cites.
Federated optimization: Distributed machine learning for on-device intelligence
Konecnỳ, J., McMahan, H. B., Ramage, D., and Richtárik, P · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al · 2016
Earlier work this paper cites.
Minimizing finite sums with the stochastic average gradient
Schmidt, M., Le Roux, N., and Bach, F · 2017
Cited alongside, same era.
Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J · 2018
Cited alongside, same era.
Lag: Lazily aggregated gradient for communication-efficient distributed learning
Chen, T., Giannakis, G. B., Sun, T., and Yin, W · 2018
Cited alongside, same era.
On the ineffectiveness of variance reduced optimization for deep learning
Defazio, A. and Bottou, L · 2018
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Lasg: Lazily aggregated stochastic gradients for communication-efficient distributed learning
Chen, T., Sun, Y., and Yin, W · 2020
Later among the works it cites.
Federated learning’s blessing: Fedavg has linear speedup
Qu, Z., Lin, K., Kalagnanam, J., Li, Z., Zhou, J., and Zhou, Z · 2020
Later among the works it cites.
Adaptive federated optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konecny, J., Kumar, S., and McMahan, H. B · 2020
Later among the works it cites.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Wang, J., Liu, Q., Liang, H., Joshi, G., and Poor, H. V · 2020
Later among the works it cites.
Minibatch vs local sgd for heterogeneous distributed learning
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Asynchronous decentralized parallel stochastic gradient descent
Lian, X., Zhang, W., Zhang, C., and Liu, J · 2018
Cited alongside, same era.
Local sgd converges fast and communicates little
Stich, S. U · 2018
Cited alongside, same era.
Wang, J. and Joshi, G · 2018
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.
First analysis of local gd on heterogeneous data
Khaled, A., Mishchenko, K., and Richtárik, P · 2019
Cited alongside, same era.
Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T · 2019
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
Cited alongside, same era.
Woodworth, B., Patel, K. K., and Srebro, N · 2020
Later among the works it cites.
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
Later among the works it cites.
Taming convergence for asynchronous stochastic gradient descent with unbounded delay in non-convex learning
Zhang, X., Liu, J., and Zhu, Z · 2020
Later among the works it cites.
Federated learning based on dynamic regularization
Acar, D. A. E., Zhao, Y., Navarro, R. M., Mattina, M., Whatmough, P. N., and Saligrama, V · 2021
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Federated learning under arbitrary communication patterns
Avdiukhin, D. and Kasiviswanathan, S · 2021
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On large-cohort training for federated learning
Charles, Z., Garrett, Z., Huo, Z., Shmulyian, S., and Smith, V · 2021
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Fast federated learning in the presence of arbitrary device unavailability
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Federated learning with buffered asynchronous aggregation
Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Esmaeili, M. M., and Huba, D · 2021
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Towards flexible device participation in federated learning
Ruan, Y., Zhang, X., Liang, S.-C., and Joe-Wong, C · 2021
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A field guide to federated optimization
Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., Al-Shedivat, M., Andrew, G., Avestimehr, S., Daly, K., Data, D., et al · 2021
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Achieving linear speedup with partial worker participation in non-{iid} federated learning
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