arXiv Open Access 2024

Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning

Xueqing Zhang Junkai Zhang Ka-Ho Chow Juntao Chen Ying Mao +4 lainnya

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Abstrak

This demo paper examines the susceptibility of Federated Learning (FL) systems to targeted data poisoning attacks, presenting a novel system for visualizing and mitigating such threats. We simulate targeted data poisoning attacks via label flipping and analyze the impact on model performance, employing a five-component system that includes Simulation and Data Generation, Data Collection and Upload, User-friendly Interface, Analysis and Insight, and Advisory System. Observations from three demo modules: label manipulation, attack timing, and malicious attack availability, and two analysis components: utility and analytical behavior of local model updates highlight the risks to system integrity and offer insight into the resilience of FL systems. The demo is available at https://github.com/CathyXueqingZhang/DataPoisoningVis.

Topik & Kata Kunci

cs.CR

Penulis (9)

Xueqing Zhang

Junkai Zhang

Ka-Ho Chow

Juntao Chen

Ying Mao

Mohamed Rahouti

Xiang Li

Yuchen Liu

Wenqi Wei

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APA MLA BibTeX

Zhang, X., Zhang, J., Chow, K., Chen, J., Mao, Y., Rahouti, M. et al. (2024). Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning. https://arxiv.org/abs/2405.16707

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Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓