KL-FedDis: A federated learning approach with distribution information sharing using Kullback-Leibler divergence for non-IID data

Data Heterogeneity or Non-IID (non-independent and identically distributed) data identification is one of the prominent challenges in Federated Learning (FL). In Non-IID data, clients have their own local data, which may not be independently and identically distributed. This arises because clients i...

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Detalhes bibliográficos

Principais autores:	Md. Rahad, Ruhan Shabab, Mohd. Sultan Ahammad, Md. Mahfuz Reza, Amit Karmaker, Md. Abir Hossain
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2025-03-01
Colecção:	Neuroscience Informatics
Assuntos:	Federated learning FedDis KL divergence Regularization
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S277252862400027X
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