Dynamic user clustering for efficient and privacy-preserving federated learning

Dynamic user clustering for efficient and privacy-preserving federated learning

With the wider adoption of machine learning and increasing concern about data privacy, federated learning (FL) has received tremendous attention. FL schemes typically enable a set of participants, i.e., data owners, to individually train a machine learning model using their local data, which are the...

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Bibliographic Details
Main Authors: Liu, Ziyao, Guo, Jiale, Yang, Wenzhuo, Fan, Jiani, Lam, Kwok-Yan, Zhao, Jun
Other Authors: College of Computing and Data Science
Format: Journal Article
Language:English
Published: 2024
Subjects:
Computer and Information Science
Federated learning
Privacy preserving aggregation
Gradient sparsification
User clustering
Online Access:https://hdl.handle.net/10356/179908

Internet

https://hdl.handle.net/10356/179908

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