CalFAT: Calibrated Federated Adversarial Training with Label Skewness

30 May 2022

Papers citing "CalFAT: Calibrated Federated Adversarial Training with Label Skewness"

8 / 8 papers shown

Title
FedCC: Robust Federated Learning against Model Poisoning Attacks Hyejun Jeong H. Son Seohu Lee Jayun Hyun T. Chung FedML 56 5 0 20 Feb 2025
Decentralized and Robust Privacy-Preserving Model Using Blockchain-Enabled Federated Deep Learning in Intelligent Enterprises Reza Fotohi Fereidoon Shams Aliee Bahar Farahani FedML 76 8 0 18 Feb 2025
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions Weiming Zhuang Chen Chen Lingjuan Lyu C. L. P. Chen Yaochu Jin Lingjuan Lyu AIFin AI4CE 99 85 0 27 Jun 2023
Delving into the Adversarial Robustness of Federated Learning Jie M. Zhang Bo-wen Li Chen Chen Lingjuan Lyu Shuang Wu Shouhong Ding Chao Wu FedML 35 34 0 19 Feb 2023
FedProto: Federated Prototype Learning across Heterogeneous Clients Yue Tan Guodong Long Lu Liu Tianyi Zhou Qinghua Lu Jing Jiang Chengqi Zhang FedML 153 459 0 01 May 2021
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Shaoxiong Ji Yue Tan Teemu Saravirta Zhiqin Yang Yixin Liu Lauri Vasankari Shirui Pan Guodong Long A. Walid FedML 34 76 0 25 Feb 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 180 355 0 07 Dec 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016