73 citations · 125 across the 7 of their papers we have counts for
7 papers
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security
Youyang Qu, Ming Liu, Tianqing Zhu +3
Federated Learning (FL) offers a promising paradigm for training Large Language Models (LLMs) in a decentralized manner while preserving data privacy and minimizing communication o…
A Survey on the Real Power of ChatGPT
Ming Liu, Ran Liu, Ye Zhu +5
ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT. A key challenge for the evaluation is that ChatGPT is still closed-source…
Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective
Youyang Qu, Lichuan Ma, Wenjie Ye +4
The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of smart healthcare networks (SHNs). To enhance the precision of di…
A Comprehensive Survey on Edge Data Integrity Verification: Fundamentals and Future Trends
Yao Zhao, Youyang Qu, Yong Xiang +3
Recent advances in edge computing~(EC) have pushed cloud-based data caching services to edge, however, such emerging edge storage comes with numerous challenging and unique securit…
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
Chenhao Xu, Youyang Qu, Tom H. Luan +3
Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Feder…
Blockchain-based Digital Twin for Supply Chain Management: State-of-the-Art Review and Future Research Directions
Jiongbin Liu, William Yeoh, Youyang Qu +1
Supply chain management (SCM) plays a vital role in the global economy, as evidenced by recent COVID-19 supply chain challenges. Traditional SCM faces security and efficiency issue…
Asynchronous Federated Learning on Heterogeneous Devices: A Survey
Chenhao Xu, Youyang Qu, Yong Xiang +1
Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of lo…