Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges
ACM Transactions on Computing for Healthcare
; 3(4) (no pagination), 2022.
Artigo
em Inglês
| EMBASE | ID: covidwho-2315801
ABSTRACT
Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.© 2022 Copyright held by the owner/author(s).
Federated learning; gdpr; transfer learning; adverse drug reaction; algorithm; Alzheimer disease; article; brain tumor; breast density; cancer research; cerebrovascular accident; clinical decision support system; convolutional neural network; coronavirus disease 2019/di [Diagnosis]; data quality; decentralization; deep learning; drug sensitivity; electronic health record; energy consumption; false negative result; false positive result; functional magnetic resonance imaging; genome-wide association study; health care; health care cost; health care industry; heart disease; human; interpersonal communication; machine learning; noise; prediction; priority journal; privacy; prognosis; quantitative structure activity relation; thorax radiography; workflow; brain computer interface
Texto completo:
Disponível
Coleções:
Bases de dados de organismos internacionais
Base de dados:
EMBASE
Idioma:
Inglês
Revista:
ACM Transactions on Computing for Healthcare
Ano de publicação:
2022
Tipo de documento:
Artigo
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