FLOP: Federated Learning on Medical Datasets using Partial Networks

By Qiang Yang, Jianyi Zhang, Weituo Hao, Gregory P. Spell, Lawrence Carin. 2021

100 citations recorded by the source corpus.

The outbreak of COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. To aid and accelerate the diagnosis process, automatic diagnosis of COVID-19 via deep learning models has recently been explored by researchers across the world. While different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19, the data itself is still scarce due to patient privacy concerns. Federated Learning (FL) is a natural solution because it allows different organizations to cooperatively learn an effective deep learning model without sharing raw data. However, recent studies show that FL still lacks privacy protection and may cause data leakage. We investigate this challenging problem by proposing a simple yet effective algorithm, named Federated Learning on Medical Datasets using Partial Networks (FLOP), that shares only a partial model between the server and clients. Extensive experiments on benchmark data and real-world healthcare tasks show that our approach achieves comparable or better performance while reducing the privacy and security risks. Of particular interest, we conduct experiments on the COVID-19 dataset and find that our FLOP algorithm can allow different hospitals to collaboratively and effectively train a partially shared model without sharing local patients' data.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Zhen Fang
2. Xiao Wang
3. Lixu Wang
4. Qi Zhu
5. Shichao Xu
6. Jiahua Dong
7. Gan Sun
8. Mingxia Liu

Citations in the corpus

• Federated Class-Incremental Learning (2022, methodology): “Recently, it has attracted significant interests in academia and achieved remarkable successes in various industrial applications, e.g. , autonomous driving [39], wearable devices [33], medical diagnosis [10,52] and mobile phones [36].”
• Federated learning for medical image analysis: A survey (2023, background): “Yang et al. [84] argue that sharing an entire model (network) may not fully protect privacy, and thus propose sharing a partial model for federated learning on medical datasets.”