Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Abstract

Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method’s parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems.