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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Ngā kaituhi matua:	N. Sivakumar, Ahmad Raza Khan, Syed Umar, R. N. Ravikumar, I. Bremnavas, Munindra Lunagaria, Krunal Vaghela, Ghanshyam G. Tejani, Sunil Kumar Sharma
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	IEEE 2025-01-01
Rangatū:	IEEE Access
Ngā marau:	Federated learning brain tumor classification FedAvg FedProx data heterogeneity privacy protection
Urunga tuihono:	https://ieeexplore.ieee.org/document/10918716/
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!