A Hybrid Brain Tumor Classification Using FL With FedAvg and FedProx for Privacy and Robustness Across Heterogeneous Data Sources

Data privacy and heterogeneity among healthcare settings present fundamental challenges to machine learning (ML) brain tumor classification (BTC) model development based on local data. In this paper, we outline the need to develop an effective brain tumor diagnosis model while ensuring data security...

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Hlavní autoři:	N. Sivakumar, Ahmad Raza Khan, Syed Umar, R. N. Ravikumar, I. Bremnavas, Munindra Lunagaria, Krunal Vaghela, Ghanshyam G. Tejani, Sunil Kumar Sharma
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2025-01-01
Edice:	IEEE Access
Témata:	Federated learning brain tumor classification FedAvg FedProx data heterogeneity privacy protection
On-line přístup:	https://ieeexplore.ieee.org/document/10918716/
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