MLSP-P71.5
From Lightweight Client Models to a Foundation Model in One Shot with Generative Distillation for Medical Image Segmentation
Hanwen Zhang, Qiaojin Shen, Yufei Ma, Denghua Li, Yifan Pan, Yuesheng Zhu, Guibo Luo, Peking University, China
Session:
MLSP-P71: Federated Learning Systems and Algorithms I Poster
Track:
Machine Learning for Signal Processing [ML]
Location:
Poster Area 4
Presentation Time:
Fri, 8 May, 09:00 - 11:00
Presentation
Discussion
Resources
No resources available.
Session MLSP-P71
MLSP-P71.1: Federated Camouflaged Poisoning Attack in Federated Unlearning
Weimin Lai, Qiao Yan, Sizhe Liang, Kai Zhong, Shenzhen University, China
MLSP-P71.2: FedDBP: Enhancing Federated Prototype Learning with Dual-Branch Features and Personalized Global Fusion
Ningzhi Gao, Siquan Huang, Leyu Shi, Ying Gao, South China University of Technology, China
MLSP-P71.3: FEDPROTOALIGN: FEDERATED PROTOTYPE ALIGNMENT UNDER IDENTITY INCONSISTENCY FOR GAIT RECOGNITION
Chen Lin, Guanghao Li, Tsinghua University, China; Qinglun Li, National University of Defense Technology, China; Yongxian Wei, Hongyang Wei, Tsinghua University, China; Li Shen, Sun Yat-Sen University, China; Ming Tang, Southern University of Science and Technology, China; Chun Yuan, Tsinghua University, China
MLSP-P71.4: DFL-ALLC: ADAPTIVE LOCAL LEARNING CONTROL FOR DECENTRALIZED FEDERATED LEARNING IN HETEROGENEOUS VEHICULAR NETWORKS
Dongyuan Su, Shenzhen Technology University, China; Laizhong Cui, Shenzhen University, China
MLSP-P71.5: From Lightweight Client Models to a Foundation Model in One Shot with Generative Distillation for Medical Image Segmentation
Hanwen Zhang, Qiaojin Shen, Yufei Ma, Denghua Li, Yifan Pan, Yuesheng Zhu, Guibo Luo, Peking University, China
MLSP-P71.6: PRETRAIN-DPFL: MITIGATING NOISE DETRIMENT IN DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH MODEL PRE-TRAINING
Huitong Jin, Shenzhen University, China; Yipeng Zhou, Quan Z. Sheng, Macquarie University, Australia; Shiting Wen, NingboTech University, China; Laizhong Cui, Shenzhen University, China
MLSP-P71.7: Communication-Efficient Federated Learning with Pre-Executed Gradient Descent
Cheng Che, State Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China, China; Lutan Zhao, Yuhui Zhang, Rui Hou, State Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, Chinese Academy of Sciences, China
MLSP-P71.8: Domination Strategies for Free-Riding in Cross-Silo FL-based Caching
Jiqing Gu, Chengdu University of Information Technology, China; Chao Song, Jianfeng Huang, University of Electronic Science and Technology of China, China; Jie Wu, Temple University, United States of America; Ruilin Hu, Li Lu, University of Electronic Science and Technology of China, China
MLSP-P71.9: DFLF: A SCALABLE DECENTRALIZED FEDERATED LEARNING FRAMEWORK BASED ON PYTORCH
Jiarong Li, Weihong Yuan, Yuhao Zhou, Qing Ye, Jiancheng Lv, Sichuan University, China
MLSP-P71.10: DCFL: DUAL END CONSTRAINT FEDERATED LEARNING WITH AN ADAPTIVE ANALYTIC ANCHOR
Xizhong Liu, Yuelin Feng, Nanfang College Guangzhou, China; Yingbiao Hu, Zhongkai University of Agriculture and Engineering, China
Contacts