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Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server.
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 · 1910
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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. 2019 · 1912
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The infinitesimal jackknife
Jaeckel, L. 1972 · 1972
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The influence curve and its role in robust estimation
Hampel, F. R. 1974 · 1974
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Detection of influential observation in linear regression
Cook, R. D. 1977 · 1977
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Characterizations of an empirical influence function for detecting influential cases in regression
Cook, R.; and Weisberg, S. 1980 · 1980
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Residuals and influence in regression
Cook, R. D.; and Weisberg, S. 1982 · 1982
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The elements of statistical learning
Friedman, J.; Hastie, T.; and Tibshirani, R. 2001 · 2001
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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 · 2002
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Understanding Black-box Predictions via Influence Functions
Koh, P. W.; and Liang, P. 2017 · 2017
Cited alongside, same era.
A tutorial on Fisher information
Ly, A.; Marsman, M.; Verhagen, J.; Grasman, R. P.; and Wagenmakers, E.-J. 2017 · 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 · 2017
Cited alongside, same era.
LEAF: A Benchmark for Federated Settings
Caldas, S.; Wu, P.; Li, T.; Konecný, J.; McMahan, H. B.; Smith, V.; and Talwalkar, A. 2018 · 2018
Cited alongside, same era.
Data Cleansing for Models Trained with SGD
Hara, S.; Nitanda, A.; and Maehara, T. 2019 · 2019
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Interpreting Black Box Predictions using Fisher Kernels
Khanna, R.; Kim, B.; Ghosh, J.; and Koyejo, S. 2019 · 2019
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On the Accuracy of Influence Functions for Measuring Group Effects
Koh, P. W.; Ang, K.; Teo, H. H. K.; and Liang, P. 2019 · 2019
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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 · 2019
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FedBoost: A Communication-Efficient Algorithm for Federated Learning
Hamer, J.; Mohri, M.; and Suresh, A. T. 2020 · 2020
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Fetchsgd: Communication-efficient federated learning with sketching
Rothchild, D.; Panda, A.; Ullah, E.; Ivkin, N.; Stoica, I.; Braverman, V.; Gonzalez, J.; and Arora, R. 2020 · 2020
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Zhao, Y.; Li, M.; Lai, L.; Suda, N.; Civin, D.; and Chandra, V. 2018 · 2018
Cited alongside, same era.
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Haddadpour, F.; Kamani, M. M.; Mahdavi, M.; and Cadambe, V. R. 2019 · 2019
Cited alongside, same era.
Federated Optimization in Heterogeneous Networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2020a
Cited in the paper.
On the Convergence of FedAvg on Non-IID Data
Li, X.; Huang, K.; Yang, W.; Wang, S.; and Zhang, Z. 2020b
Cited in the paper.
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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 · 2020
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