3 papers
eess.SP2026
Smartphone-Based Identification of Unknown Liquids via Active Vibration Sensing
Yongzhi Huang
Traditional liquid identification instruments are often unavailable to the general public. This paper shows the feasibility of identifying unknown liquids with commercial lightweig…
cs.LG2025
A Fast and Flat Federated Learning Method via Weighted Momentum and Sharpness-Aware Minimization
Tianle Li, Yongzhi Huang, Linshan Jiang +5
In federated learning (FL), models must \emph{converge quickly} under tight communication budgets while \emph{generalizing} across non-IID client distributions. These twin requirem…
cs.LG2025
FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios
Tianle Li, Yongzhi Huang, Linshan Jiang +5
Federated Learning (FL) enables decentralized model training while preserving data privacy. Despite its benefits, FL faces challenges with non-identically distributed (non-IID) dat…