Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning
<|reference_start|>Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning: 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.<|reference_end|>
@article{zhang2024visualizing,
    title={Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated
  Learning},
    author={Xueqing Zhang, Junkai Zhang, Ka-Ho Chow, Juntao Chen, Ying Mao,
  Mohamed Rahouti, Xiang Li, Yuchen Liu, Wenqi Wei},
    journal={arXiv preprint arXiv:2405.16707},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16707},
    primaryClass={cs.CR}
}