Multi-Task Learning for Federated Classification and Regression

doi:doi:10.57702/ye0h42cx

Multi-Task Learning for Federated Classification and Regression

The proposed algorithm allows personalizing the learning model for each participant without sharing the training data and improves the performance, compared to that of the locally trained models provide. The method is especially beneﬁcial in the case of the low volumes of data available to individual participants.

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Aleksei Ponomarenko-Timofeev, Olga Galinina, Ravikumar Balakrishnan, Nageen Himayat, Sergey Andreev, Yevgeni Koucheryavy (2024). Dataset: Multi-Task Learning for Federated Classification and Regression. https://doi.org/10.57702/ye0h42cx

DOI retrieved: December 16, 2024

Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.2303.10254
Author	Aleksei Ponomarenko-Timofeev
More Authors	Olga Galinina Ravikumar Balakrishnan Nageen Himayat Sergey Andreev Yevgeni Koucheryavy