Elastic-Trust Hybrid Federated Learning
學年 114
學期 1
出版（發表）日期 2025-11-01
作品名稱 Elastic-Trust Hybrid Federated Learning
作品名稱（其他語言）
著者 Yi-Cheng Chen; Lin Hui; Yung-Lin Chu
單位
出版者
著錄名稱、卷期、頁數 Computer Science and Information Systems 22(4), p.1777-1796
摘要 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.
關鍵字 machine learning; federated learning; decentralization; hybrid federated integration
語言 en
ISSN 1820-0214; 2406-1018
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者
審稿制度 是
國別 SRB
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128677 )