FedDSL: A Novel Client Selection Method to Handle Statistical Heterogeneity in Cross-Silo Federated Learning Using Flower Framework
Federated learning provides a mechanism for different silos to collaborate, and each silo gets aid without compromising privacy. This simulation study is based on healthcare datasets, so the silos are hospitals or healthcare organizations. The selection of hospitals for federated learning increases...
Saved in:
| Main Authors: | Vineetha Pais, Santhosha Rao, Balachandra Muniyal |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10720779/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review
by: Vineetha Pais, et al.
Published: (2025-01-01) -
SMART-FL: Single-Shot Merged Adaptive Resource-Aware Tensor-Fusion for Efficient Federated Learning in Heterogeneous Cross-Silo Environments
by: Vineetha Pais, et al.
Published: (2025-01-01) -
Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
by: Aiswariya Milan Kummaya, et al.
Published: (2025-02-01) -
Bridging silos through governance innovations: the role of the EU cities mission
by: Alexandra Buylova, et al.
Published: (2025-01-01) -
ANÁLISE ESTRUTURAL DE PAINÉIS METÁLICOS PARA SILOS PRISMÁTICOS
by: José Wallace Barbosa do Nascimento, et al.