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arXiv · 2405.16707

Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning

Xueqing Zhang·Junkai Zhang·Ka-Ho Chow·Juntao Chen·Ying Mao·Mohamed Rahouti·Xiang Li·Yuchen Liu·Wenqi Wei
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Abstract

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.

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Xueqing Zhang, Junkai Zhang, Ka-Ho Chow, Juntao Chen, Ying Mao, Mohamed Rahouti, Xiang Li, Yuchen Liu, Wenqi Wei. 2024-05-26. Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning. https://arxiv.org/abs/2405.16707

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