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Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing.
Mnist handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 1998
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Distributed subgradient methods for multi-agent optimization
Nedic, A., and Ozdaglar, A · 2009
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Optimal distributed online prediction using mini-batches
Dekel, O., Gilad-Bachrach, R., Shamir, O., and Xiao, L · 2012
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S., and Lan, G · 2013
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Accelerating stochastic gradient descent using predictive variance reduction
Johnson, R., and Zhang, T · 2013
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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On the convergence of decentralized gradient descent
Yuan, K., Ling, Q., and Yin, W · 2016
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J · 2017
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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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Harnessing smoothness to accelerate distributed optimization
Qu, G., and Li, N · 2017
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Federated learning of predictive models from federated electronic health records
Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., and Shi, W · 2018
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Spider: near-optimal non-convex optimization via stochastic path integrated differential estimator
Fang, C., Li, C. J., Lin, Z., and Zhang, T · 2018
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Don’t use large mini-batches, use local sgd
Lin, T., Stich, S. U., Patel, K. K., and Jaggi, M · 2018
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On the convergence of federated optimization in heterogeneous networks
Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., and Smith, V · 2018
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Local sgd converges fast and communicates little
Stich, S. U · 2018
Cited alongside, same era.
On nonconvex decentralized gradient descent
Zeng, J., and Yin, W · 2018
Cited alongside, same era.
Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2018
Cited alongside, same era.
On the convergence of local descent methods in federated learning
Haddadpour, F., and Mahdavi, M · 2019
Cited alongside, same era.
Communication efficient decentralized training with multiple local updates
Li, X., Yang, W., Wang, S., and Zhang, Z · 2019
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2020
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Decentralized federated learning for electronic health records
Lu, S., Zhang, Y., and Wang, Y · 2020
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Distributed stochastic gradient tracking methods
Pu, S., and Nedić, A · 2020
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The error-feedback framework: Better rates for sgd with delayed gradients and compressed updates
Stich, S. U., and Karimireddy, S. P · 2020
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Federated learning for healthcare informatics
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., and Wang, F · 2020
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Fltrust: Byzantine-robust federated learning via trust bootstrapping
Cao, X., Fang, M., Liu, J., and Gong, N. Z · 2021
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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
Cited alongside, same era.
Gnsd: A gradient-tracking based nonconvex stochastic algorithm for decentralized optimization
Lu, S., Zhang, X., Sun, H., and Hong, M · 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.
Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
Cited alongside, same era.
On the linear speedup analysis of communication efficient momentum sgd for distributed non-convex optimization
Yu, H., Jin, R., and Yang, 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.
Periodic stochastic gradient descent with momentum for decentralized training
Gao, H., and Huang, H · 2020
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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 · 2021
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Achieving optimal sample and communication complexities for non-iid federated learning
Khanduri, P., Sharma, P., Yang, H., Hong, M., Liu, J., Rajawat, K., and Varshney, P. K · 2021
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Cooperative sgd: A unified framework for the design and analysis of local-update sgd algorithms
Wang, J., and Joshi, G · 2021
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An improved convergence analysis for decentralized online stochastic non-convex optimization
Xin, R., Khan, U. A., and Kar, S · 2021
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Achieving linear speedup with partial worker participation in non-i.i.d. federated learning
Yang, H., Fang, M., and Liu, J · 2021
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Cfedavg: achieving efficient communication and fast convergence in non-iid federated learning
Yang, H., Liu, J., and Bentley, E. S · 2021
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Anarchic federated learning
Yang, H., Zhang, X., Khanduri, P., and Liu, J · 2022
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