539 citations · 768 across the 4 of their papers we have counts for
5 papers
Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer
Shiyao Ma, Jiangtian Nie, Jiawen Kang +5
With the rapid development of cloud manufacturing, industrial production with edge computing as the core architecture has been greatly developed. However, edge devices often suffer…
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design
Zhe Zhang, Shiyao Ma, Jiangtian Nie +4
Federated Learning (FL) allows edge devices (or clients) to keep data locally while simultaneously training a shared high-quality global model. However, current research is general…
Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms
Yi Liu, Jiangtian Nie, Xuandi Li +3
Due to air quality significantly affects human health, it is becoming increasingly important to accurately and timely predict the Air Quality Index (AQI). To this end, this paper p…
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Yi Liu, Sahil Garg, Jiangtian Nie +4
Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming…
A Stackelberg Game Approach Towards Socially-Aware Incentive Mechanisms for Mobile Crowdsensing (Online report)
Jiangtian Nie, Jun Luo, Zehui Xiong +2
Mobile crowdsensing has shown a great potential to address large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participat…