| Texto completo | |
| Autor(es): |
Nasajpour, Mohammad
;
Pouriyeh, Seyedamin
;
Parizi, Reza M.
;
Han, Meng
;
Mosaiyebzadeh, Fatemeh
;
Xie, Yixin
;
Liu, Liyuan
;
Batista, Daniel Macedo
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INFORMATION; v. 16, n. 6, p. 31-pg., 2025-06-12. |
| Resumo | |
Machine learning has brought about a revolutionary transformation in healthcare. It has traditionally been employed to create predictive models through training on locally available data. However, privacy concerns can sometimes impede the collection and integration of data from diverse sources. Conversely, a lack of sufficient data may hinder the construction of accurate models, thereby limiting the ability to produce meaningful outcomes. Especially in the field of healthcare, collecting datasets centrally is challenging due to privacy concerns. Indeed, federated learning (FL) emerges as a sophisticated distributed machine learning approach that comes to the rescue in such scenarios. It allows multiple devices hosted at different institutions, like hospitals, to collaboratively train a global model without sharing raw data. In addition, each device retains its data securely on locally, addressing the challenges of time-consuming annotation and privacy concerns. In this paper, we conducted a comprehensive literature review aimed at identifying the most advanced federated learning applications in cancer research and clinical oncology analysis. Our main goal was to present a comprehensive overview of the development of federated learning in the field of oncology. Additionally, we discuss the challenges and future research directions. (AU) | |
| Processo FAPESP: | 14/50937-1 - INCT 2014: da Internet do Futuro |
| Beneficiário: | Fabio Kon |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| 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: | 15/24485-9 - Internet do futuro aplicada a cidades inteligentes |
| Beneficiário: | Fabio Kon |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |