Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity

Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity

Federated learning (FL) enables deep learning models to be trained locally on devices without the need for data sharing, ensuring data privacy. However, when clients have uneven or imbalanced data distributions, it leads to data heterogeneity. Data heterogeneity can appear in different ways, often d...

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Bibliographic Details
Main Authors: Aiswariya Milan Kummaya, Amudha Joseph, Kumar Rajamani, George Ghinea
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Applied System Innovation
Subjects:
federated learning
data heterogeneity
quantity skew
label distribution skew
image skew
Fed-Hetero
Online Access:https://www.mdpi.com/2571-5577/8/2/28
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