DOE OSTI · 3375464
Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy
Abstract
High-energy Two dimensional (2D) synchrotron x-ray diffractometry provides important insights into the atomistic structure and phase evolution of materials, yet traditional analysis methods remain complex, knowledge-intensive, and computationally demanding. Deep-learning models offer a powerful alternative for automating their analysis. Institutions that hold these datasets may be unwilling to share their data due to privacy and security policies, as well as the challenges associated with large-scale data transfer. As a result, models trained on local datasets often perform well only on their own data but exhibit bias and poor generalization across different instruments or facilities. To overcome these limitations, we explore federated learning (FL) for 2D synchrotron diffractograms, enabling collaborative model training without exchanging raw data. In this study, 2D synchrotron diffractograms of Ti–6Al–4V alloy collected from two independent facilities are used to train convolutional neural networks for predicting the β-phase volume fraction. Experimental results show that federated global models significantly outperform locally trained models in terms of generalization and achieve accuracy comparable to centralized trained models. These findings demonstrate the potential of FL to enable secure, cross-institutional collaboration and enhance the scalability of deep-learning-based materials characterization.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Yue, Weiqi [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000232537991), Guo, Qingzhe [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0009000712996684), Mehdi, Redad [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000205935222), Ponon, Gabriel [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000293666199), Dernek, Ozan [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000250719684), Olatunde, Ayorinde E. [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0009000968296016), Tripathi, Pawan K. [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000286047744), Whitney, Bonnie [Worcester Polytechnic Institute, MA (United States)] (ORCID:0000000152869494), Spangenberger, Anthony [Worcester Polytechnic Institute, MA (United States)] (ORCID:0000000279409778), Lados, Diana A. [Worcester Polytechnic Institute, MA (United States)] (ORCID:0000000319031563), Brown, Donald W. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000345658212), Clausen, Bjorn [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000033906846X), Samanta, Amit [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:000000033620987X), Ernst, Frank [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000248232041), Willard, Matthew A. [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000150528012), Ayday, Erman [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000333831081), French, Roger H. [Case Western Reserve Univ., Cleveland, OH (United States)] (ORCID:0000000261620532). 2026-04-07. Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy. https://doi.org/10.1088/2632-2153%2Fae55f8
Cite the original work for its findings. Save a collection to share your selection of sources.