Contribution-Aware Federated Learning for Smart Healthcare


  • Zelei Liu Nanyang Technological University
  • Yuanyuan Chen Nanyang Technological University
  • Yansong Zhao Nanyang Technological University
  • Han Yu Nanyang Technological University
  • Yang Liu Tsinghua University
  • Renyi Bao Yidu Cloud Technology Inc.
  • Jinpeng Jiang Yidu Cloud Technology Inc.
  • Zaiqing Nie Tsinghua University
  • Qian Xu WeBank
  • Qiang Yang WeBank Hong Kong University of Science and Technology



Smart Healthcare, Federated Learning, Contribution Evaluation


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 (CAFL) framework for smart healthcare. It provides an efficient and accurate approach to fairly evaluate FL participants' contribution to model performance without exposing their private data, and improves the FL model training protocol to allow the best performing intermediate models to be distributed to participants for FL training. Since its deployment in Yidu Cloud Technology Inc. in March 2021, CAFL has served 8 well-established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations 2.84 times faster than the best existing approach, and has improved the average accuracy of the resulting models by 2.62% compared to the previous system (which is significant in industrial settings). To our knowledge, it is the first contribution-aware federated learning successfully deployed in the healthcare industry.




How to Cite

Liu, Z., Chen, Y., Zhao, Y., Yu, H., Liu, Y., Bao, R., Jiang, J., Nie, Z., Xu, Q., & Yang, Q. (2022). Contribution-Aware Federated Learning for Smart Healthcare. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12396-12404.