2 citations · 2 across the 9 of their papers we have counts for
9 papers
DynaFilter: Cloud-driven Dynamic Filtering for Satellite Edge Intelligence
Ziyang Zhang, Jie Liu, Luca Mottola
Modern satellite edge systems, including those performing remote sensing tasks such object detection and tracking, are characterized by severely limited bandwidth and intermittent…
Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games
Haoran Li, Zengle Ge, Ziyang Zhang +10
Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs. However, applying these methods to adversarial multi…
CANS: Accelerating Multiuser Collaborative Edge Inference via Cooperative Autodidactic NeuroSurgeon
Zheshun Wu, Ziyang Zhang, Changyao Lin +2
Recently, mobile edge computing (MEC)-enabled collaborative deep neural network (DNN) inference has emerged as a promising approach for delivering intelligent services to resource-…
SparseDVFS: Sparse-Aware DVFS for Energy-Efficient Edge Inference
Ziyang Zhang, Zheshun Wu, Jie Liu +1
Deploying deep neural networks (DNNs) on power-sensitive edge devices presents a formidable challenge. While Dynamic Voltage and Frequency Scaling (DVFS) is widely employed for ene…
SparOA: Sparse and Operator-aware Hybrid Scheduling for Edge DNN Inference
Ziyang Zhang, Jie Liu, Luca Mottola
The resource demands of deep neural network (DNN) models introduce significant performance challenges, especially when deployed on resource-constrained edge devices. Existing solut…
E4: Energy-Efficient DNN Inference for Edge Video Analytics Via Early-Exit and DVFS
Ziyang Zhang, Yang Zhao, Ming-Ching Chang +2
Deep neural network (DNN) models are increasingly popular in edge video analytic applications. However, the compute-intensive nature of DNN models pose challenges for energy-effici…
Debiasing Federated Learning with Correlated Client Participation
Zhenyu Sun, Ziyang Zhang, Zheng Xu +3
In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averagin…
DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference
Ziyang Zhang, Yang Zhao, Huan Li +2
Due to limited resources on edge and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy c…
BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms
Ziyang Zhang, Huan Li, Yang Zhao +2
As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-…