I’m Zirui Gong, a PhD candidate at Griffith University, working in TrustAGI Lab, under the supervision of Dr. Leo Zhang, Prof. Shirui Pan, and Dr. Yanjun Zhang. My research is centered on uncovering security and privacy vulnerabilities in Federated Learning (FL) systems, with the goal of improving the resilience and trustworthiness of distributed machine learning.

Publications

• Zirui Gong, et al. “ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery”, in IEEE Symposium on Security and Privacy 2026 (S&P).
• Chen, Z., Zirui Gong, et al. “Beyond Denial-of-Service: The Puppeteer’s Attack for Fine-Grained Control in Ranking-Based Federated Learning”, in Proceedings of the Web Conference 2026 (WWW 2026).
• Zirui Gong, et al. “Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning”, in IEEE Symposium on Security and Privacy 2025 (S&P).
• Zirui Gong, et al. “Agramplifier: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification”, IEEE Transactions on Information Forensics and Security (TIFS), vol. 19, 2023, pp. 1241–1250.
• Li, Yuqi*, Zirui Gong*, et al. “Production Evaluation of Citrus Fruits Based on the YOLOv5 Compressed by Knowledge Distillation”, in International Conference on Computer Supported Cooperative Work in Design (CSCWD).

Teaching

At Griffith University, I work as a Teaching Assistant for courses such as Trustworthy AI, Deep Learning, Ethical Hacking, Application Systems, and Programming Principles. Additionally, I contribute as a Course Content Designer for Security Operations Centres & AI, helping students build both theoretical and practical expertise.

Research Interests

• Federated Learning Security
• Privacy-Preserving Machine Learning
• Adversarial Attacks & Defenses
• Secure Distributed Systems

Contact

• 📧 Email: zirui.gong@griffithuni.edu.au
• 📍 Location: Gold Coast, QLD, Australia
• 🔗 Google Scholar