Yue, Weiqi https://orcid.org/0000-0002-3253-7991
Guo, Qingzhe https://orcid.org/0009-0007-1299-6684
Mehdi, Redad https://orcid.org/0000-0002-0593-5222
Ponon, Gabriel https://orcid.org/0000-0002-9366-6199
Dernek, Ozan https://orcid.org/0000-0002-5071-9684
Olatunde, Ayorinde E https://orcid.org/0009-0009-6829-6016
Tripathi, Pawan K https://orcid.org/0000-0002-8604-7744
Whitney, Bonnie https://orcid.org/0000-0001-5286-9494
Spangenberger, Anthony https://orcid.org/0000-0002-7940-9778
Lados, Diana A https://orcid.org/0000-0003-1903-1563
Brown, Donald W
Clausen, Bjorn https://orcid.org/0000-0003-3906-846X
Samanta, Amit https://orcid.org/0000-0003-3620-987X
Ernst, Frank https://orcid.org/0000-0002-4823-2041
Willard, Matthew A https://orcid.org/0000-0001-5052-8012
Ayday, Erman https://orcid.org/0000-0003-3383-1081
French, Roger H https://orcid.org/0000-0002-6162-0532
Funding for this research was provided by:
National Science Foundation (DMR-1829070)
Lawrence Livermore National Laboratory (DE-AC52-07NA27344)
Department of Energy’s National Nuclear Security Administration (DE-NA0004104)
Article Title: Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy
Journal Title: Machine Learning: Science and Technology
Article Type: paper
Copyright Information: © 2026 The Author(s). Published by IOP Publishing Ltd
Publication dates
Date Received: 2025-12-02
Date Accepted: 2026-03-23
Online publication date: 2026-04-07