Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data

Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data

Due to the distributed data collection and learning in federated learnings, many clients conduct local training with non-independent and identically distributed (non-IID) datasets. Accordingly, the training from these datasets results in severe performance degradation. We propose an efficient algori...

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Bibliographic Details
Main Authors: Mihye Seol, Taejoon Kim
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Sensors
Subjects:
federated learning
non-IID data
class imbalance mitigation
Online Access:https://www.mdpi.com/1424-8220/23/3/1152

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https://www.mdpi.com/1424-8220/23/3/1152

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