Transfer Learning In Cross-Domain Data Environments: Methods And Applications

Abstract

Transfer learning is used to advance the generalization across heterogeneous domains, but is faced with distribution shifts, feature mismatch, few labelled data and privacy concerns in distributed environments. The cross domain transfer learning model that this research proposes is based on the Golden Sine Algorithm (GSA) and BERT with federated learning (FL) for privacy-aware training. GSA effectively optimizes hyperparameters to enhance convergence and performance, while BERT captures rich contextual representations from input data. FL allows for the secure distributed training process without sharing raw data. Based on the above, this research introduces a cross domain transfer learning model with the GSA and BERT combined with FL for privacy-preserving training on the MovieLens 20M dataset and the Book-Crossing dataset. The key mechanisms are: (1) min-max normalization to scale feature spaces, (2) a central mechanism to pull in knowledge, and (3) effective adaptation across different heterogeneous domains by fine-tuning the target domain. Experimental results with Python (version 3.10) implementation prove to be better than baseline methods, with the Recall@10 (Movie) and H@10 (Movie) results of 0.0837 and 0.7486 respectively, and Recall@10 (Book) and H@10 (Book) of 0.0677 and 0.6842 respectively, indicating better learning efficiency, adaptability, and predictive accuracy. Prospective cross-domain federated model based on transformer is scalable, privacy-preserving, and robust, applicable to healthcare, smart manufacturing, and structural health monitoring applications.