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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| Główni autorzy: | , , , , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
Elsevier
2025-03-01
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| Seria: | Neuroscience Informatics |
| Hasła przedmiotowe: | |
| Dostęp online: | http://www.sciencedirect.com/science/article/pii/S277252862400027X |
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