6 papers · 1 filter
Joint Memory Frequency and Computing Frequency Scaling for Energy-efficient DNN Inference
Yunchu Han, Zhaojun Nan, Sheng Zhou +1
Deep neural networks (DNNs) have been widely applied in diverse applications, but the problems of high latency and energy overhead are inevitable on resource-constrained devices. T…
FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen +3
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume…
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
Tan Chen, Jintao Yan, Yuxuan Sun +2
Federated learning (FL) is a promising paradigm for multiple devices to cooperatively train a model. When applied in wireless networks, two issues consistently affect the performan…
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
Jintao Yan, Tan Chen, Yuxuan Sun +3
Asynchronous Federated Learning (AFL) enables distributed model training across multiple mobile devices, allowing each device to independently update its local model without waitin…
DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysis
Yunchu Han, Zhaojun Nan, Sheng Zhou +1
The rapid development of deep neural networks (DNNs) is inherently accompanied by the problem of high computational costs. To tackle this challenge, dynamic voltage frequency scali…
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
Jintao Yan, Tan Chen, Yuxuan Sun +3
Leveraging the computing and sensing capabilities of vehicles, vehicular federated learning (VFL) has been applied to edge training for connected vehicles. The dynamic and intercon…