Improved Algorithm for Image Classification Datasets Using Federated Transfer Learning.

The advancement of machine learning is significantly influenced by the accessibility of vast data. However, in reality, data is distributed across various entities and is subject to various legal and practical constraints. Therefore, it is unrealistic to merge these dispersed datasets into a single large dataset. In order to address this challenge, a novel framework called Federated Transfer Learning (FTL) has been introduced. FTL allows knowledge sharing without jeopardizing user privacy and enables the transfer of complementary knowledge across different domains. This capability enables the use of rich labels from the source domain to construct an effective model tailored to the target domain’s specific needs. In this paper, we propose an improved algorithm, named FTL-FIC, based on FTL for image classification datasets. It leverages federated learning to aggregate data and utilizes transfer learning to construct an shared learning model, thereby adapting to the characteristics of the target dataset. Experimental results demonstrate the algorithm’s notable improvement in the classification accuracy of the target dataset (increased by 8.17%), the reduced number of convergence epochs, and its ability to maintain good generalization on the source dataset. Furthermore, FTL-FIC exhibits versatility and scalability, indicating its potential application to other types of datasets.