Federated Learning Models using Flow Cytometry Data of Blood Test in Medical Decision Support
Eunjeong Park, Hyo Suk Nam, Jaewoo Song · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Medical big data has become important as many hospitals have been collecting massive amounts of medical information in daily treatment. We investigated the architecture of federated learning to construct the detection model of disease with blood test data formatted in flow cytometry standards to facilitate multi-site medical research. The big data characteristics of raw information in blood tests and privacy problems in sharing patient data make it hard to collect and share data into the central site to construct the generalized detection model. In this paper, we introduce the work-in-progress study, FedM-FCM, the federated learning of flow cytometry analysis with the pipeline from the data sources to the domain-shifted distribution of the federated learning model. We compose the major components of FedM-FCM with data representation of multi-dimensional flow cytometry, adoption of neural network models based on the data representation, and aggregation and distribution of learning parameters across the participating hospitals without data sharing.