1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.DC2025
Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
Jinqi Yan, Fang He, Qianlong Sang +5
Dynamic Voltage and Frequency Scaling is essential for enhancing energy efficiency in mobile platforms. However, traditional heuristic-based governors are increasingly inadequate f…
cs.LG2025
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…
cs.DC2022★ 1 cited
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…