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

• Project/Area Number: 25K00373
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 60060:Information network-related
• Research Institution: Hosei 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: ¥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)
• Keywords: Federated 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.