  Recent progress in material data mining has been driven by high-capacity
models trained on large datasets. However, collecting experimental data has
been extremely costly owing to the amount of human effort and expertise
required. Therefore, material researchers are often reluctant to easily
disclose their private data, which leads to the problem of data island, and it
is difficult to collect a large amount of data to train high-quality models. In
this study, a material microstructure image feature extraction algorithm
FedTransfer based on data privacy protection is proposed. The core
contributions are as follows: 1) the federated learning algorithm is introduced
into the polycrystalline microstructure image segmentation task to make full
use of different user data to carry out machine learning, break the data island
and improve the model generalization ability under the condition of ensuring
the privacy and security of user data; 2) A data sharing strategy based on
style transfer is proposed. By sharing style information of images that is not
urgent for user confidentiality, it can reduce the performance penalty caused
by the distribution difference of data among different users.
