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We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model.
Fictitious play property for games with identical interests
Monderer, D. and Shapley, L. S · 1996
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Vowpal wabbit online learning project
Langford, J., Li, L., and Strehl, A · 2007
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Feature hashing for large scale multitask learning
Weinberger, K. Q., Dasgupta, A., Langford, J., Smola, A. J., and Attenberg, J · 2009
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Slow learners are fast
Zinkevich, M., Langford, J., and Smola, A. J · 2009
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On the rate of convergence of fictitious play
Brandt, F., Fischer, F., and Harrenstein, P · 2010
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Distributed delayed stochastic optimization
Agarwal, A. and Duchi, J. C · 2011
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Libsvm: A library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
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Dual averaging for distributed optimization: Convergence analysis and network scaling
Duchi, J. C., Agarwal, A., and Wainwright, M. J · 2011
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Contextual bandit learning with predictable rewards
Agarwal, A., Dudík, M., Kale, S., Langford, J., and Schapire, R · 2012
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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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On the convergence of alternating minimization for convex programming with applications to iteratively reweighted least squares and decomposition schemes
Beck, A · 2015
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Making contextual decisions with low technical debt
Agarwal, A., Bird, S., Cozowicz, M., Hoang, L., Langford, J., Lee, S., Li, J., Melamed, D., Oshri, G., Ribas, O., et al · 2016
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Federated meta-learning with fast convergence and efficient communication
Chen, F., Luo, M., Dong, Z., Li, Z., and He, X · 2018
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Practical contextual bandits with regression oracles
Foster, D. J., Agarwal, A., Dudík, M., Luo, H., and Schapire, R. E · 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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Improving federated learning personalization via model agnostic meta learning
Jiang, Y., Konečnỳ, J., Rush, K., and Kannan, S · 2019
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Scaffold: Stochastic controlled averaging for on-device federated learning
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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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Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
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Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T · 2019
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Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2019
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Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T · 2019
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