MLSP-P39.1
PERSONALIZED FEDERATED LEARNING BASED ON CLUSTERING KNOWLEDGE PROTOTYPE ALIGNMENT AND DISTRIBUTION-AWARE CONSISTENCY
Menglin Yang, Xinyun Zhang, Peng Wang, Da Liu, Jun Zhou, Southwest University, China
Session:
MLSP-P39: Federated and Distributed Machine Learning Systems II Poster
Track:
Machine Learning for Signal Processing [ML]
Location:
Poster Area 8
Presentation Time:
Wed, 6 May, 16:30 - 18:30
Presentation
Discussion
Resources
No resources available.
Session MLSP-P39
MLSP-P39.1: PERSONALIZED FEDERATED LEARNING BASED ON CLUSTERING KNOWLEDGE PROTOTYPE ALIGNMENT AND DISTRIBUTION-AWARE CONSISTENCY
Menglin Yang, Xinyun Zhang, Peng Wang, Da Liu, Jun Zhou, Southwest University, China
MLSP-P39.2: DISTRIBUTION-AWARE MOBILITY-ASSISTED DECENTRALIZED FEDERATED LEARNING
Md Farhamdur Reza, Reza Jahani, NC State University, United States of America; Richeng Jin, Zhejiang University, China; Huaiyu Dai, NC State University, China
MLSP-P39.3: FEDPLA: PROTOTYPE-ALIGNED LOW-RANK ADAPTATION FOR MULTIMODAL FEDERATED LEARNING
Chaofeng Li, Ruichun Gu, Inner Mongolia University of Science and Technology, China
MLSP-P39.4: VAE-GENERATED SECOND-ORDER GLOBAL PROTOTYPES FOR HETEROGENEOUS FEDERATED LEARNING
Mingyu Pang, Yongqiang Gao, Yongmei Liu, Bo Ma, Hualong Cui, Inner Mongolia University, China
MLSP-P39.5: DOMAIN-ADAPTIVE MODEL MERGING ACROSS DISCONNECTED MODES
Junming Liu, Yusen Zhang, Tongji University, China; Rongchao Zhang, Peking University, China; Wenkai Zhu, Southeast University, China; Tian Wu, Nanchang University, China
MLSP-P39.6: DSA: DIRECTION AND SIGN ALIGNMENT FOR CONTRIBUTION EVALUATION IN FEDERATED LEARNING
Lingfu Wang, Tong Wu, Guangchun Luo, Aiguo Chen, University of Electronic Science and Technology of China, China
MLSP-P39.7: Averaging is Not Enough: Preserving Client-Specific Knowledge in Federated PEFT with One-Round Aggregation
Haoran Cheng, Jiahui Huang, University of Science and Technology of China, China; Quanchao Liu, China Mobile Research Institute, China; Lan Zhang, University of Science and Technology of China, China
MLSP-P39.8: FEDGPAI: PERSONALIZED FEDERATED LEARNING BASED ON PARAMETER SENSITIVITY ADAPTIVE INTERPOLATION
Yaole Li, Tao Liu, Kuiming Wang, Jiguang Lv, Huanran Wang, Yuyan Shi, Shuai Han, Wu Yang, Harbin Engineering University, China
MLSP-P39.9: MGHFED: ENHANCING HETEROGENEOUS SUBGRAPH FEDERATED LEARNING THROUGH ADVERSARIAL META-PATH GENERATION
Xingbo Zhang, Jinguo Hu, Hong Peng, Li Tang, Yunnan University, China
MLSP-P39.10: LAYER-WISE CONTRIBUTION EVALUATION FOR INCENTIVIZING PERSONALIZATION IN FEDERATED LEARNING
Xuanjing Zhang, Minghao Yao, Qi Guo, Saiyu Qi, Yong Han, Yue Yang, Yong Qi, Yanan Qiao, Xi’an Jiaotong University, China
Contacts