539 citations · 1.5k across the 6 of their papers we have counts for
10 papers
Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things
Yi Liu, Ruihui Zhao, Jiawen Kang +3
Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT), which signific…
Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework
Jiawen Kang, Zehui Xiong, Chunxiao Jiang +6
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" proble…
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…
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
Yi Liu, Xingliang Yuan, Zehui Xiong +3
As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally belie…
A Secure Federated Learning Framework for 5G Networks
Yi Liu, Jialiang Peng, Jiawen Kang +3
Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintain…