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Multi-Agent Federated Learning-based Rumor Detection and Its Robust Optimization in Social Networks

Research Project

Project/Area Number 25K00373
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 60060:Information network-related
Research InstitutionHosei University

Principal Investigator

余 恪平  法政大学, 理工学研究科, 准教授 (40779104)

Project Period (FY) 2025-04-01 – 2028-03-31
Project Status Granted (Fiscal Year 2025)
Budget Amount *help
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2027: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2026: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2025: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
KeywordsFederated Learning / Rumor Detection / Social Networks
Outline of Research at the Start

Online social networks are complex and unpredictable, characterized by diverse data and dynamic user behaviors. This project uses multi-agent federated learning to detect rumors more efficiently and robustly, with a focus on feature representation, model building, and robust optimization.

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Published: 2025-05-07   Modified: 2025-06-20  

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