MLSP-L2.6

ADVERSARIAL UPDATE-BASED FEDERATED UNLEARNING FOR POISONED MODEL RECOVERY

Wenwei Zhao, Xiaowen Li, Yao Liu, Zhuo Lu, University of South Florida, United States of America
Session:
MLSP-L2: Personalized and Secure Federated Learning Oral
Track:
Machine Learning for Signal Processing [ML]
Location:
Room 113
Presentation Time:
Tue, 5 May, 15:40 - 16:00
Presentation
Discussion
Resources
No resources available.
Session MLSP-L2
MLSP-L2.1: D-LoRA: A Dual Low-Rank Adaptation Framework for Cost-Efficient Personalized Federated Learning
Hongyan Gu, Jiang Li, Mengyao Peng, Xinyi Zhang, Yedi Ma, Xinli Huang, East China Normal University, China
MLSP-L2.2: HETEROGENEOUS ADVERSARIAL FEDERATED LEARNING
Wenrui Wang, Nankai University, China; Liping Yi, Tianjin University, China; Gang Wang, Xiaoguang Liu, Nankai University, China
MLSP-L2.3: APPLE: ATTENTION-PROMOTED PROTOTYPE LEARNING FOR FEDERATED CROSS-MODAL HASHING
Xing Tian, Yifan Zeng, Zihao Zhan, South China Normal University, China; Yuanyuan Li, Wing W. Y. Ng, South China University of Technology, China
MLSP-L2.4: TURNING DATA HETEROGENEITY INTO A BACKDOOR SHIELD FOR PERSONALIZED FEDERATED LEARNING
Qi Cui, Xuebo Wang, Xianyi Chen, Qianqian Wang, Zhangjie Fu, Nanjing University of Information Science and Technology, China
MLSP-L2.5: DPMM-CFL: CLUSTERED FEDERATED LEARNING VIA DIRICHLET PROCESS MIXTURE MODEL NONPARAMETRIC CLUSTERING
Mariona Jaramillo-Civill, Peng Wu, Pau Closas, Northeastern University, United States of America
MLSP-L2.6: ADVERSARIAL UPDATE-BASED FEDERATED UNLEARNING FOR POISONED MODEL RECOVERY
Wenwei Zhao, Xiaowen Li, Yao Liu, Zhuo Lu, University of South Florida, United States of America
Contacts