709 citations · 2.7k across the 86 of their papers we have counts for
8 papers · 1 filter
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
Peichun Li, Guoliang Cheng, Xumin Huang +4
In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjus…
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition
Kan Xie, Zhe Zhang, Bo Li +4
With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules…
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
Zonghang Li, Yihong He, Hongfang Yu +4
Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate o…
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones
Zhe Zhang, Shiyao Ma, Zhaohui Yang +5
Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastr…
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…
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…
Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Yi Liu, James J. Q. Yu, Jiawen Kang +2
Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of datasets gathered by governments and organizations. Howeve…
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
Jiawen Kang, Zehui Xiong, Dusit Niyato +3
To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedl…