12 citations · 16 across the 5 of their papers we have counts for
8 papers
Federated Learning Hyper-Parameter Tuning from a System Perspective
Huanle Zhang, Lei Fu, Mi Zhang +4
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-…
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
Lei Fu, Huanle Zhang, Ge Gao +2
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia. In a typical…
MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification
Rex Liu, Huanle Zhang, Hamed Pirsiavash +1
We propose MASTAF, a Model-Agnostic Spatio-Temporal Attention Fusion network for few-shot video classification. MASTAF takes input from a general video spatial and temporal represe…
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning
Huanle Zhang, Nicharee Wisuthiphaet, Hemiao Cui +3
The past decade witnesses a rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the a…
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective
Huanle Zhang, Mi Zhang, Xin Liu +2
Federated learning (FL) hyper-parameters significantly affect the training overheads in terms of computation time, transmission time, computation load, and transmission load. Howev…
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers
Rex Liu, Sarina A Fazio, Huanle Zhang +3
With the development of the Internet of Things(IoT) and Artificial Intelligence(AI) technologies, human activity recognition has enabled various applications, such as smart homes a…