Suppressing Poisoning Attacks on Federated Learning for Medical Imaging-Reference-Cited by-同舟云学术

Suppressing Poisoning Attacks on Federated Learning for Medical Imaging

Published:2022 Issue: Volume: Page:673-683
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Alkhunaizi Naif^ORCID,Kamzolov Dmitry^ORCID,Takáč Martin^ORCID,Nandakumar Karthik^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-16452-1_64

Reference34 articles.

1. PyTorch: an imperative style, high-performance deep learning library

2. Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. In: International Conference on Artificial Intelligence and Statistics, pp. 2938–2948. PMLR (2020)

3. Bhagoji, A.N., Chakraborty, S., Mittal, P., Calo, S.: Model poisoning attacks in federated learning. In: Proceedings of Workshop on Security Machine Learning (SecML) 32nd Conference Neural Information Processing Systems (NeurIPS), pp. 1–23 (2018)

4. Bhagoji, A.N., Chakraborty, S., Mittal, P., Calo, S.: Analyzing federated learning through an adversarial lens. In: International Conference on Machine Learning, pp. 634–643. PMLR (2019)

5. Blanchard, P., El Mhamdi, E.M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: Byzantine tolerant gradient descent. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

Cited by 9 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. Byzantine-robust federated learning with ensemble incentive mechanism;Future Generation Computer Systems;2024-10

2. Generating Synthetic Data for Medical Imaging;Radiology;2024-09-01

3. Federated learning for medical image analysis: A survey;Pattern Recognition;2024-07

4. TMN: An Efficient Robust Aggregator for Federated Learning;Lecture Notes in Electrical Engineering;2024

5. Federated learning in healthcare applications;Data Fusion Techniques and Applications for Smart Healthcare;2024