Cloud–Edge–End Collaborative Federated Learning: Enhancing Model Accuracy and Privacy in Non-IID Environments

Cloud–Edge–End Collaborative Federated Learning: Enhancing Model Accuracy and Privacy in Non-IID Environments

Cloud–edge–end computing architecture is crucial for large-scale edge data processing and analysis. However, the diversity of terminal nodes and task complexity in this architecture often result in non-independent and identically distributed (non-IID) data, making it challenging to balance data hete...

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Bibliographic Details
Main Authors: Ling Li, Lidong Zhu, Weibang Li
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
cloud–edge–end architecture
federated learning
privacy preserving
edge computing
Online Access:https://www.mdpi.com/1424-8220/24/24/8028
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