Elastic-trust hybrid federated learning

Owing to the widespread application of machine learning, increasing attention has been focused on extensive data collection for learning model construction. Recently, with growing concerns about data privacy, private information protection has significantly increased the operation cost and difficulty of boosting model performance. The Federated Learning (FL) technique has been introduced to address this issue by keeping data on client devices and reducing the need to handle sensitive data directly. However, several challenging issues may arise when applying FL, such as data heterogeneity, efficient feature transmission, and additional computational demands. In this study, a novel FL model, Elastic-Trust Hybrid Federated Learning (ET-FL), is introduced with a dual federated learning framework. ET-FL incorporates the trust mechanism and differential aggregation strategy for model optimization and computation reduction. In addition, the proposed model is applied on real-world datasets to show the performance and practicability of promising results.