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Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL).
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.
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Wang, J., Liu, Q., Liang, H., Joshi, G., and Poor, H. V · 2007
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Cho, Y. J., Wang, J., and Joshi, G · 2010
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Practical secure aggregation for federated learning on user-held data
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2016
Earlier work this paper cites.
EMNIST: an extension of MNIST to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Federated learning for mobile keyboard prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
Earlier work this paper cites.
Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
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Applied federated learning: Improving google keyboard query suggestions
Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., and Beaufays, F · 2018
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Qsparse-local-sgd: Distributed sgd with quantization, sparsification, and local computations
Basu, D., Data, D., Karakus, C., and Diggavi, S · 2019
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Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecny, J., Mazzocchi, S., McMahan, H. B., Overveldt, T. V., Petrou, D., Ramage, D., and Roselander, J · 2019
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Semi-cyclic stochastic gradient descent
Eichner, H., Koren, T., McMahan, B., Srebro, N., and Talwar, K · 2019
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Active federated learning
Goetz, J., Malik, K., Bui, D., Moon, S., Liu, H., and Kumar, A · 2019
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Convergence rate of incremental gradient and incremental newton methods
Gurbuzbalaban, M., Ozdaglar, A., and Parrilo, P. A · 2019
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On the convergence of local descent methods in federated learning
Haddadpour, F. and Mahdavi, M · 2019
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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
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Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H., and Brown, M · 2019
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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., D’Oliveira, R. G. L., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascon, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konecny, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Ozgur, A., Pagh, R., Raykova, M., Qi, H., Ramage, D., Raskar, R., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramer, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Sgd without replacement: Sharper rates for general smooth convex functions
Nagaraj, D., Jain, P., and Netrapalli, P · 2019
Cited alongside, same era.
Local SGD converges fast and communicates little
Stich, S. U · 2019
Cited alongside, same era.
Convergence of random reshuffling under the kurdyka- { \{ \ \backslash L } \} ojasiewicz inequality
Li, X., Milzarek, A., and Qiu, J · 2021
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Random reshuffling with variance reduction: New analysis and better rates
Malinovsky, G., Sailanbayev, A., and Richtárik, P · 2021
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A unified convergence analysis for shuffling-type gradient methods
Nguyen, L. M., Tran-Dinh, Q., Phan, D. T., Nguyen, P. H., and Van Dijk, M · 2021
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Federated evaluation and tuning for on-device personalization: System design & applications
Paulik, M., Seigel, M., Mason, H., Telaar, D., Kluivers, J., van Dalen, R., Lau, C. W., Carlson, L., Granqvist, F., Vandevelde, C., et al · 2021
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Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
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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.
Sgd with shuffling: optimal rates without component convexity and large epoch requirements
Ahn, K., Yun, C., and Sra, S · 2020
Cited alongside, same era.
Privacy amplification via random check-ins
Balle, B., Kairouz, P., McMahan, B., Thakkar, O., and Guha Thakurta, A · 2020
Cited alongside, same era.
Linear convergence of gradient and proximal-gradient methods under the polyak-Łojasiewicz condition
Karimi, H., Nutini, J., and Schmidt, M · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Random reshuffling: Simple analysis with vast improvements
Mishchenko, K., Khaled, A., and Richtárik, P · 2020
Cited alongside, same era.
Federated learning’s blessing: Fedavg has linear speedup
Qu, Z., Lin, K., Kalagnanam, J., Li, Z., Zhou, J., and Zhou, Z · 2020
Cited alongside, same era.
Closing the convergence gap of sgd without replacement
Rajput, S., Gupta, A., and Papailiopoulos, D · 2020
Cited alongside, same era.
Qu, Z., Lin, K., Kalagnanam, J., Li, Z., Zhou, J., and Zhou, Z · 2021
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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 · 2021
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Random shuffling beats sgd only after many epochs on ill-conditioned problems
Safran, I. and Shamir, O · 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
Yang, H., Fang, M., and Liu, J · 2021
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Diurnal or nocturnal? federated learning of multi-branch networks from periodically shifting distributions
Zhu, C., Xu, Z., Chen, M., Konečnỳ, J., Hard, A., and Goldstein, T · 2021
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Multi-epoch matrix factorization mechanisms for private machine learning
Choquette-Choo, C. A., McMahan, H. B., Rush, K., and Thakurta, A · 2022
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Papaya: Practical, private, and scalable federated learning
Huba, D., Nguyen, J., Malik, K., Zhu, R., Rabbat, M., Yousefpour, A., Wu, C.-J., Zhan, H., Ustinov, P., Srinivas, H., et al · 2022
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Fedvarp: Tackling the variance due to partial client participation in federated learning
Jhunjhunwala, D., Sharma, P., Nagarkatti, A., and Joshi, G · 2022
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Federated learning with formal differential privacy guarantees
McMahan, B. and Thakurta, A · 2022
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On the unreasonable effectiveness of federated averaging with heterogeneous data
Wang, J., Das, R., Joshi, G., Kale, S., Xu, Z., and Zhang, T · 2022
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A unified analysis of federated learning with arbitrary client participation
Wang, S. and Ji, M · 2022
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Minibatch vs local sgd with shuffling: Tight convergence bounds and beyond
Yun, C., Rajput, S., and Sra, S · 2022
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Communication-efficient local sgd with age-based worker selection
Zhu, F., Zhang, J., and Wang, X · 2022
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