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Federated learning is a setting where agents, each with access to their own data source, combine models from local data to create a global model.
An introduction to multivariate statistical analysis
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Illustrating empirical Bayes methods
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The Stability of Hedonic Coalition Structures
Bogomolnaia, A.; and Jackson, M. O. 2002 · 2001
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Learning from data: a short course: AMLbook
Abu-Mostafa, Y.; Lin, H.; and Magdon-Ismail, M. 2012 · 2012
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A Game-Theoretic Approach to Coalition Formation in Green Cloud Federations
Guazzone, M.; Anglano, C.; and Sereno, M. 2014 · 2014
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Parameter inference with estimated covariance matrices
Sellentin, E.; and Heavens, A. F. 2015 · 2015
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Collaborative PAC Learning
Blum, A.; Haghtalab, N.; Procaccia, A. D.; and Qiao, M. 2017 · 2017
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Learning-from-data-Solutions
Paquay, P. 2018 · 2018
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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.; Gascón, 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.; Konečný, J.; Korolova, A.; Koushanfar, F.; Koyejo, S.; Lepoint, T.; Liu, Y.; Mittal, P.; Mohri, M.; Nock, R.; Özgür, A.; Pagh, R.; Raykova, M.; Qi, H.; Ramage, D.; Raskar, R.; Song, D.; Song, W.; Stich, S. U.; Sun, Z.; Suresh, A. T.; Tramèr, F.; Vepakomma, P.; Wang, J.; Xiong, L.; Xu, Z.; Yang, Q.; Yu, F. X.; Yu, H.; and Zhao, S. 2019 · 2019
Cited alongside, same era.
Fair Resource Allocation in Federated Learning
Li, T.; Sanjabi, M.; Beirami, A.; and Smith, V. 2019 · 2019
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Unpublished work, private correspondence
Blum, A.; Haghtalab, N.; Shao, H.; and Phillips, R. L. 2020 · 2020
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Three Approaches for Personalization with Applications to Federated Learning
Mansour, Y.; Mohri, M.; Ro, J.; and Suresh, A. T. 2020 · 2020
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Minimax Pareto Fairness: A Multi Objective Perspective
Martinez, N.; Bertran, M.; and Sapiro, G. 2020 · 2020
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Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints
Sattler, F.; Muller, K.-R.; and Samek, W. 2020 · 2020
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The Communication-Aware Clustered Federated Learning Problem
Shlezinger, N.; Rini, S.; and Eldar, Y. C. 2020 · 2020
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To Split or Not to Split: The Impact of Disparate Treatment in Classification
Wang, H.; Hsu, H.; Diaz, M.; and Calmon, F. P. 2020 · 2020
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Salvaging Federated Learning by Local Adaptation
Yu, T.; Bagdasaryan, E.; and Shmatikov, V. 2020 · 2020
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Where Is the Normative Proof? Assumptions and Contradictions in ML Fairness Research
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Adaptive Personalized Federated Learning
Deng, Y.; Kamani, M. M.; and Mahdavi, M. 2020 · 2020
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Federated Learning: Challenges, Methods, and Future Directions
Li, T.; Sahu, A. K.; Talwalkar, A.; and Smith, V. 2020 · 2020
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Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems
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