3 citations · 6 across the 4 of their papers we have counts for
4 papers
Dynamic Clustering for Personalized Federated Learning on Heterogeneous Edge Devices
Heting Liu, Junzhe Huang, Fang He +1
Federated Learning (FL) enables edge devices to collaboratively learn a global model, but it may not perform well when clients have high data heterogeneity. In this paper, we propo…
HoneyIoT: Adaptive High-Interaction Honeypot for IoT Devices Through Reinforcement Learning
Chongqi Guan, Heting Liu, Guohong Cao +2
As IoT devices are becoming widely deployed, there exist many threats to IoT-based systems due to their inherent vulnerabilities. One effective approach to improving IoT security i…
Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization
Heting Liu, Fang He, Guohong Cao
Federated learning (FL) enables geographically dispersed edge devices (i.e., clients) to learn a global model without sharing the local datasets, where each client performs gradien…
Prediction of GPU Failures Under Deep Learning Workloads
Heting Liu, Zhichao Li, Cheng Tan +4
Graphics processing units (GPUs) are the de facto standard for processing deep learning (DL) tasks. Meanwhile, GPU failures, which are inevitable, cause severe consequences in DL t…