73 citations · 100 across the 9 of their papers we have counts for
9 papers
Electrically Pumped Terahertz Frequency Comb Based on Actively Mode-locked Resonant Tunneling Diode
Feifan Han, Xiongbin Yu, Qun Zhang +6
Terahertz (THz) frequency combs (TFCs) are promising for numerous applications in spectroscopy, metrology, sensing, and wireless communications. However, the practical applications…
Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
Tian Wu, Liming Wang, Zijian Wen +5
The emergence of Mixture-of-Experts (MoE) has transformed the scaling of large language models by enabling vast model capacity through sparse activation. Yet, converting these perf…
Broadband Terahertz Frequency Comb Based on Actively Mode Locked Resonant Tunneling Diode
Feifan Han, Hongxin Zhou, Qun Zhang +9
The frequency combs characterized by their phase-coherent equidistant spectral modes and precise frequency scales of broadband spectrum, have made them an indispensable part of con…
TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction
Weijie Liu, Ziwei Zhan, Carlee Joe-Wong +5
Non-independent and identically distributed (Non-IID) data across edge clients have long posed significant challenges to federated learning (FL) training in edge computing environm…
Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning
Bokeng Zheng, Bo Rao, Tianxiang Zhu +5
Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living.Meanw…
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Weijie Liu, Xiaoxi Zhang, Jingpu Duan +3
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works…
Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm
Yihong Li, Xiaoxi Zhang, Tianyu Zeng +4
Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each ta…
NFVactor: A Resilient NFV System using the Distributed Actor Model
Jingpu Duan, Xiaodong Yi, Shixiong Zhao +3
Resilience functionality, including failure resilience and flow migration, is of pivotal importance in practical network function virtualization (NFV) systems. However, existing fa…
Dynamic Scaling of Virtualized, Distributed Service Chains: A Case Study of IMS
Jingpu Duan, Chuan Wu, Franck Le +2
The emerging paradigm of network function virtualization advocates deploying virtualized network functions (VNF) on standard virtualization platforms for significant cost reduction…