WMDefense: Using Watermark to Defense Byzantine Attacks in Federated Learning-Reference-Cited by-同舟云学术

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WMDefense: Using Watermark to Defense Byzantine Attacks in Federated Learning

Published:2022-05-02 Issue: Volume: Page:
ISSN:
Container-title:IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
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Author:

Zheng Xu¹,Dong Qihao¹,Fu Anmin¹

Affiliation:

1. Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9797887/9797888/09798217.pdf?arnumber=9798217

Reference23 articles.

1. Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach

2. Analyzing federated learning through an adversarial lens[C];bhagoji;International Conference on Machine Learning,2019

3. FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

4. Verifiable Privacy-preserving Scheme based on Vertical Federated Random Forest

5. Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning

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1. FedCRMW: Federated model ownership verification with compression-resistant model watermarking;Expert Systems with Applications;2024-09

2. BadCleaner: Defending Backdoor Attacks in Federated Learning via Attention-Based Multi-Teacher Distillation;IEEE Transactions on Dependable and Secure Computing;2024-09

3. Design of Anti-Plagiarism Mechanisms in Decentralized Federated Learning;IEEE Transactions on Services Computing;2024-07

4. Decentralized Federated Learning Links for Biometric Recognition;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. A Generic Blockchain-based Steganography Framework with High Capacity via Reversible GAN;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

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