Federated Learning-Based Synchrotron X-ray Microdiffraction Image Screening for Industry Materials

Bo Chen, Kang Xu, Yongxin Zhu, Li Tian, Victor Chang

Research output: Contribution to journalArticlepeer-review

Abstract

Synchrotron X-ray microdiffraction (μXRD) services are conducted for industrial minerals to identify their crystal impurities in terms of crystallinity and potential impurities. μXRD services generate huge loads of images that have to be screened before further processing and storage. However, there are insufficient effective labeled samples to train a screening model since service consumers are unwilling to share their original experimental images. In this paper, we propose a physics law-informed federated learning (FL) based μXRD image screening method to improve the screening while protecting data privacy. In our method, we handle the unbalanced data distribution challenge incurred by service consumers with different categories and amounts of samples with novel client sampling algorithms. We also propose hybrid training schemes to handle asynchronous data communications between FL clients and servers. The experiments show that our method can ensure effective screening for industrial users conducting industrial material testing while keeping commercially confidential information.
Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusE-pub ahead of print - 22 Sep 2022

Keywords

  • X-ray imaging
  • Industries
  • Collaborative work
  • Synchrotron radiation
  • X-ray lasers
  • X-ray scattering
  • Servers

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