Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
Zirui Gong, Yanjun Zhang, Leo Yu Zhang, Zhaoxi Zhang, Yong Xiang, Shirui Pan
IEEE Symposium on Security and Privacy 2025 · Day 2 · ML Attacks and Defenses
This talk, presented by Zirui Gong from Griffith University, delves into the often-overlooked security vulnerabilities within **Federated Learning (FL)** frameworks, specifically focusing on a purportedly robust variant known as **Fed-Ranking Learning (FRL)**. The research, a collaborative effort between Griffith University, University of Technology Sydney, and Deakin University, challenges the prevailing assumption of FRL's resilience against client-side attacks. It identifies a critical vulnerability that allows malicious clients to significantly impact the global model despite the framework's inherent defenses.