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 of varying data quality and heterogeneous data distributions across multiple institutions in this sector. In this paper, we report our experience developing and deploying the Contribution-Aware Federated Learning (CAreFL) framework for smart healthcare. It provides an efficient and accurate approach to fairly evaluate FL participants’ contributions to model performance without exposing their private data, and improves the FL model training protocol by allowing the best performing intermediate sub-models to be distributed to participants for FL training. Since its deployment by Yidu Cloud Technology Inc. in March 2021, CAreFL has served eight well-established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations close to three times faster than the best existing approach and has improved the average accuracy of the resulting models by more than 2% compared to the previous system (which is significant in industrial settings). To the best of our knowledge, it is the first CAreFL successfully deployed in the healthcare industry.