CAreFL: enhancing smart healthcare with contribution-aware federated learning

• Main Authors: Liu, Zelei, Chen, Yuanyuan, Zhao, Yansong, Yu, Han, Liu, Yang, Bao, Renyi, Jiang, Jinpeng, Nie, Zaiqing, Xu, Qian, Yang, Qiang
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Data Quality; Health Care Application
• Online Access: https://hdl.handle.net/10356/171138
• Institution: Nanyang Technological University

Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to build models for smart healthcare applications. Existing deployed FL frameworks cannot address the key issues...