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In federated learning, differences in the data or objectives between the participating nodes motivate approaches to train a personalized machine learning model for each node.
Federated evaluation of on-device personalization
Wang, K., Mathews, R., Kiddon, C., Eichner, H., Beaufays, F., and Ramage, D · 1910
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Estimation with quadratic loss
James, W. and Stein, C · 1961
Earlier work this paper cites.
Three Approaches for Personalization with Applications to Federated Learning
Mansour, Y., Mohri, M., Ro, J., and Suresh, A. T · 2002
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Adaptive Personalized Federated Learning
Deng, Y., Kamani, M. M., and Mahdavi, M · 2003
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Model-sharing games: Analyzing federated learning under voluntary participation
Donahue, K. and Kleinberg, J · 2010
Earlier work this paper cites.
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Konečný, J., McMahan, H. B., Ramage, D., and Richtárik, P · 2016
Cited alongside, same era.
Improving Efficiency by Shrinkage: The James–Stein and Ridge Regression Estimators
Gruber, M · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Personalized federated learning: A meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
Federated Learning: Challenges, Methods, and Future Directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2020
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Ensemble distillation for robust model fusion in federated learning
Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
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Distributed gradient methods for convex machine learning problems in networks: Distributed optimization
Nedic, A · 2020
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Model fusion via optimal transport
Singh, S. P. and Jaggi, M · 2020
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Doubly robust off-policy evaluation with shrinkage
Su, Y., Dimakopoulou, M., Krishnamurthy, A., and Dudik, M · 2020
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Cited alongside, same era.
Weight erosion: An update aggregation scheme for personalized collaborative machine learning
Grimberg, F., Hartley, M.-A., Jaggi, M., and Karimireddy, S. P · 2020
Cited alongside, same era.