| Texto completo | |
| Autor(es): |
Nasajpour, Mohammad
;
Pouriyeh, Seyedamin
;
Parizi, Reza M.
;
Han, Meng
;
Mosaiyebzadeh, Fatemeh
;
Liu, Liyuan
;
Xie, Yixin
;
Batista, Daniel Macedo
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | ELECTRONICS; v. 14, n. 9, p. 40-pg., 2025-04-25. |
| Resumo | |
In recent years, novel technologies in smart healthcare systems have opened significant opportunities for diagnosis and treatment across various medical fields. Federated Learning (FL), a decentralized machine learning approach, trains shared models using local data from devices like wearables and hospital systems without transferring sensitive information, offering a promising solution to privacy challenges in areas such as cancer prediction, COVID-19 detection, drug discovery, and medical image processing. This literature survey reviews FL architectures (e.g., FedHealth, PerFit), applications, and recent advancements, demonstrating their impact on healthcare through enhanced predictive models for patient care. Key findings include improved accuracy in wearable-based diagnostics and secure multi-institutional collaboration, though limitations persist. We also highlight open challenges, such as security risks, communication costs, and data heterogeneity, which require further research attention. (AU) | |
| Processo FAPESP: | 21/06995-0 - Starling: segurança e alocação de recursos em B5G via técnicas de inteligência artificial |
| Beneficiário: | Daniel Macêdo Batista |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 14/50937-1 - INCT 2014: da Internet do Futuro |
| Beneficiário: | Fabio Kon |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 15/24485-9 - Internet do futuro aplicada a cidades inteligentes |
| Beneficiário: | Fabio Kon |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |