12 citations · 16 across the 9 of their papers we have counts for
9 papers
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Shiwei Li, Wenchao Xu, Haozhao Wang +7
Federated learning is a promising distributed machine learning paradigm that can effectively exploit large-scale data without exposing users' privacy. However, it may incur signifi…
ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data
Doncheng Yuan, Jianzhe Xue, Jinshan Su +2
Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing…
Erasing Radio Frequency Fingerprints via Active Adversarial Perturbation
Zhaoyi Lu, Wenchao Xu, Ming Tu +3
Radio Frequency (RF) fingerprinting is to identify a wireless device from its uniqueness of the analog circuitry or hardware imperfections. However, unlike the MAC address which ca…
OTAS: An Elastic Transformer Serving System via Token Adaptation
Jinyu Chen, Wenchao Xu, Zicong Hong +4
Transformer model empowered architectures have become a pillar of cloud services that keeps reshaping our society. However, the dynamic query loads and heterogeneous user requireme…
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration
Yunfeng Fan, Wenchao Xu, Haozhao Wang +2
Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data. Current F…
Towards Unbiased Training in Federated Open-world Semi-supervised Learning
Jie Zhang, Xiaosong Ma, Song Guo +1
Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled dat…