Joint client-and-sample selection for federated learning via bi-level optimization

Joint client-and-sample selection for federated learning via bi-level optimization

Federated Learning (FL) enables massive local data owners to collaboratively train a deep learning model without disclosing their private data. The importance of local data samples from various data owners to FL models varies widely. This is exacerbated by the presence of noisy data that exhibit lar...

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Bibliographic Details
Main Authors: Li, Anran, Wang, Guangjing, Hu, Ming, Sun, Jianfei, Zhang, Lan, Tuan, Luu Anh, Yu, Han
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Computer and Information Science
Bi-level optimization
Federated learning
Online Access:https://hdl.handle.net/10356/181061

Internet

https://hdl.handle.net/10356/181061

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