MLSP-L14.5
IMPORTANCE SAMPLING BASED FEDERATED UNSUPERVISED REPRESENTATION LEARNING
Nazreen Shah, Prachi Goyal, Ranjitha Prasad, IIIT Delhi, India
Session:
MLSP-L14: Distributed and Federated Learning I Lecture
Track:
Machine Learning for Signal Processing
Location:
Room E3
Presentation Time:
Thu, 18 Apr, 09:40 - 10:00 (UTC +9)
Session Co-Chairs:
Han Yu, Nanyang Technological University and Sheng Li, National Institute of Information and Communications Technology (NICT) Japan
Session MLSP-L14
MLSP-L14.1: UNIDEAL: CURRICULUM KNOWLEDGE DISTILLATION FEDERATED LEARNING
Yuwen Yang, Chang Liu, Xun Cai, Suizhi Huang, Hongtao Lu, Yue Ding, Shanghai Jiao Tong University, China
MLSP-L14.2: FEDERATED CINN CLUSTERING FOR ACCURATE CLUSTERED FEDERATED LEARNING
Yuhao Zhou, Minjia Shi, Yuxin Tian, Sichuan University, China; Yuanxi Li, University of Illinois at Urbana-Champaign, United States of America; Qing Ye, Jiancheng Lv, Sichuan University, China
MLSP-L14.3: FAIRNESS-AWARE JOB SCHEDULING FOR MULTI-JOB FEDERATED LEARNING
Yuxin Shi, Han Yu, Nanyang Technological University, Singapore
MLSP-L14.4: Personalized Federated Learning with Attention-based Client Selection
Zihan Chen, Jundong Li, Cong Shen, University of Virginia, United States of America
MLSP-L14.5: IMPORTANCE SAMPLING BASED FEDERATED UNSUPERVISED REPRESENTATION LEARNING
Nazreen Shah, Prachi Goyal, Ranjitha Prasad, IIIT Delhi, India
MLSP-L14.6: Communication Efficient Private Federated Learning Using Dithering
Burak Hasircioglu, Deniz Gündüz, Imperial College London, United Kingdom of Great Britain and Northern Ireland
Contacts