STDLens: Model Hijacking-Resilient Federated Learning for Object Detection-Reference-Cited by-同舟云学术

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STDLens: Model Hijacking-Resilient Federated Learning for Object Detection

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
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Author:

Chow Ka-Ho¹,Liu Ling¹,Wei Wenqi¹,Ilhan Fatih¹,Wu Yanzhao¹

Affiliation:

1. Georgia Instutite of Technology,Atlanta,GA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10205357.pdf?arnumber=10205357

Reference28 articles.

1. Internet of video things in 2030: A world with many cameras

2. Communication-efficient learning of deep networks from decentralized data;mcmahan;Artificial Intelligence and Statistics,0

3. Yolov3: An incremental improvement;redmon;ArXiv Preprint,2018

4. FLAME: Taming backdoors in federated learning;due nguyen;USENIX Security Symposium,2022

5. Federated learning in adversarial settings;kerkouche;ArXiv Preprint,2020

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data Poisoning and Leakage Analysis in Federated Learning;Springer Optimization and Its Applications;2024-09-04

2. Demo: Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning;2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS);2024-07-23

3. Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance;ACM Computing Surveys;2024-02-07

4. Federated Learning in Computer Vision;IEEE Access;2023

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