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

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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Bibliographic Details
Main Authors: Md. Rahad, Ruhan Shabab, Mohd. Sultan Ahammad, Md. Mahfuz Reza, Amit Karmaker, Md. Abir Hossain
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Neuroscience Informatics
Subjects:
Federated learning
FedDis
KL divergence
Regularization
Online Access:http://www.sciencedirect.com/science/article/pii/S277252862400027X
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