Privacy and robustness in federated learning: attacks and defenses

Privacy and robustness in federated learning: attacks and defenses

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative...

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Bibliographic Details
Main Authors: Lyu, Lingjuan, Yu, Han, Ma, Xingjun, Chen, Chen, Sun, Lichao, Zhao, Jun, Yang, Qiang, Yu, Philip S.
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering
Federated Learning
Privacy
Online Access:https://hdl.handle.net/10356/164531

Internet

https://hdl.handle.net/10356/164531

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