Towards Well-trained Model Robustness in Federated Learning: An Adversarial- Example-Generation- Efficiency Perspective
Author:
Affiliation:
1. Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University,P. R. China
2. Renmin University of China,P. R. China
3. Toronto Metropolitan University,Toronto,ON,Canada
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10622104/10622158/10622742.pdf?arnumber=10622742
Reference17 articles.
1. Communication-Efficient Learning of Deep Networks from Decentralized Data;McMahan,2023
2. PASS: A Parameter Audit-Based Secure and Fair Federated Learning Scheme Against Free-Rider Attack
3. CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning
4. InFEDge: A Blockchain-Based Incentive Mechanism in Hierarchical Federated Learning for End-Edge-Cloud Communications
5. PA-iMFL: Communication-Efficient Privacy Amplification Method Against Data Reconstruction Attack in Improved Multilayer Federated Learning
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