Data privacy protection in microscopic image analysis for material data mining

TL;DR

This paper introduces FedTransfer, a federated learning-based method for microstructure image analysis that enhances data privacy, reduces data sharing issues, and improves model generalization in material data mining.

Contribution

The study proposes a novel federated learning algorithm with style transfer for microstructure image segmentation, addressing data privacy and heterogeneity challenges.

Findings

Improved model generalization through federated learning.

Effective data privacy protection in microstructure image analysis.

Reduced performance loss via style transfer-based data sharing.

Abstract

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.