12 citations · 62 across the 39 of their papers we have counts for
13 papers · 1 filter
Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
Yuhao Pan, Xiucheng Wang, Fushuo Huo +2
Radio frequency fingerprint identification (RFFI) is a key technique for wireless network security, leveraging intrinsic hardware imperfections to enable transmitter identification…
Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
Tuo Zhang, Ning Li, Xin Yuan +4
With the breakthrough progress of large language models (LLMs) in natural language processing and multimodal tasks, efficiently deploying them on resource-constrained edge devices…
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
Yichen Li, Haozhao Wang, Wenchao Xu +9
Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-st…
A QoE-Aware Split Inference Accelerating Algorithm for NOMA-based Edge Intelligence
Xin Yuan, Ning Li, Quan Chen +3
Even the AI has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate. Thus, the model spl…
Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
Jianzhe Xue, Dongcheng Yuan, Yu Sun +5
The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intellig…
Disentangle Estimation of Causal Effects from Cross-Silo Data
Yuxuan Liu, Haozhao Wang, Shuang Wang +4
Estimating causal effects among different events is of great importance to critical fields such as drug development. Nevertheless, the data features associated with events may be d…
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…
DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning
Yunfeng Fan, Wenchao Xu, Haozhao Wang +3
Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfort…
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers
Jinyu Chen, Wenchao Xu, Song Guo +3
Federated Learning (FL) is an emerging paradigm that enables distributed users to collaboratively and iteratively train machine learning models without sharing their private data.…
PMR: Prototypical Modal Rebalance for Multimodal Learning
Yunfeng Fan, Wenchao Xu, Haozhao Wang +2
Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations. However, existing MML methods often optimi…
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model
Tao Guo, Song Guo, Junxiao Wang +1
Quick global aggregation of effective distributed parameters is crucial to federated learning (FL), which requires adequate bandwidth for parameters communication and sufficient us…
Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations
Tao Guo, Song Guo, Jiewei Zhang +2
Recently, the enactment of privacy regulations has promoted the rise of the machine unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise unlearni…