52 citations · 67 across the 11 of their papers we have counts for
11 papers
Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity
Jing Liu, Zhengliang Guo, Yan Wang +4
Federated learning (FL) is severely challenged by non-independent and identically distributed (non-IID) client data, a problem that degrades global model performance, especially in…
Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation
Zhengwei Ni, Zhidu Li, Wei Chen +4
Emerging network paradigms and applications increasingly rely on federated learning (FL) to enable collaborative intelligence while preserving privacy. However, the sustainability…
A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks
Jiaqi Wu, Jing Liu, Yang Liu +6
The proliferation of Internet of things (IoT) devices in smart cities, transportation, healthcare, and industrial applications, coupled with the explosive growth of AI-driven servi…
A Novel Approach to Differential Privacy with Alpha Divergence
Yifeng Liu, Zehua Wang
As data-driven technologies advance swiftly, maintaining strong privacy measures becomes progressively difficult. Conventional -differential privacy, while prevalent, exhib…
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
Jing Liu, Yao Du, Kun Yang +8
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources…
CLIP-Optimized Multimodal Image Enhancement via ISP-CNN Fusion for Coal Mine IoVT under Uneven Illumination
Shuai Wang, Shihao Zhang, Jiaqi Wu +7
Clear monitoring images are crucial for the safe operation of coal mine Internet of Video Things (IoVT) systems. However, low illumination and uneven brightness in underground envi…
Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment
Jiaqi Wu, Shihao Zhang, Simin Chen +7
Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios. However, existing edge detection methods face challenge…
FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models
Jiaqi Wu, Simin Chen, Yuzhe Yang +6
In recent years, large language models (LLMs) have significantly advanced the field of natural language processing (NLP). By fine-tuning LLMs with data from specific scenarios, the…
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
Minghui Chen, Meirui Jiang, Xin Zhang +3
Federated learning (FL) is a learning paradigm that enables collaborative training of models using decentralized data. Recently, the utilization of pre-trained weight initializatio…
Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment
Jiaqi Wu, Simin Chen, Zehua Wang +4
As the volume of image data grows, data-oriented cloud computing in Internet of Video Things (IoVT) systems encounters latency issues. Task-oriented edge computing addresses this b…
Blockchain-empowered Data-driven Networks: A Survey and Outlook
Xi Li, Zehua Wang, Victor C. M. Leung +3
The paths leading to future networks are pointing towards a data-driven paradigm to better cater to the explosive growth of mobile services as well as the increasing heterogeneity…