35 citations · 47 across the 11 of their papers we have counts for
12 papers
Efficient KV Cache Migration for Geo-Distributed LLM Inference in Collaborative Edge Computing
Mingjin Zhang, Jiannong Cao, Tao Wu +2
Large Language Models (LLMs) have shown exceptional capabilities but rely heavily on centralized cloud GPU clusters, leading to high bandwidth costs, latency, and privacy issues. E…
SGS: Structured Sparse Gaussian Streaming for Efficient Free-Viewpoint Video Reconstruction on Edge-IoT Devices
Yiwei Li, Jiannong Cao, Weixun Gao +4
Streaming reconstruction of Free-Viewpoint Videos (FVVs) supports immersive Internet of Things (IoT) services, such as telepresence and digital twin visualization. Existing methods…
FactCheck: Feasibility-aware Long-term Action Anticipation with Multi-agent Collaboration
Rui Cao, Jiannong Cao, Bo Yuan +2
Long-term action anticipation (LTA) aims to predict an ordered sequence of future verb-noun actions from a partially observed video. While this task serves as the foundation for em…
FedRD: Reducing Divergences for Generalized Federated Learning via Heterogeneity-aware Parameter Guidance
Kaile Wang, Jiannong Cao, Yu Yang +2
Heterogeneous federated learning (HFL) aims to ensure effective and privacy-preserving collaboration among different entities. As newly joined clients require significant adjustmen…
PolyLink: A Blockchain Based Decentralized Edge AI Platform for LLM Inference
Hongbo Liu, Jiannong Cao, Bo Yang +7
The rapid advancement of large language models (LLMs) in recent years has revolutionized the AI landscape. However, the deployment model and usage of LLM services remain highly cen…
FollowUpBot: An LLM-Based Conversational Robot for Automatic Postoperative Follow-up
Chen Chen, Jianing Yin, Jiannong Cao +5
Postoperative follow-up plays a crucial role in monitoring recovery and identifying complications. However, traditional approaches, typically involving bedside interviews and manua…
EdgeShard: Efficient LLM Inference via Collaborative Edge Computing
Mingjin Zhang, Jiannong Cao, Xiaoming Shen +1
Large language models (LLMs) have shown great potential in natural language processing and content generation. However, current LLMs heavily rely on cloud computing, leading to pro…
Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
Mingjin Zhang, Jiannong Cao, Yuvraj Sahni +2
Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural Network (DNN) models in proximity to the sources of data. To enable the training of larg…
Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing
Xiangchun Chen, Jiannong Cao, Zhixuan Liang +2
Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute microservices from end devices. Microservice offloading, a fu…
ENTS: An Edge-native Task Scheduling System for Collaborative Edge Computing
Mingjin Zhang, Jiannong Cao, Lei Yang +3
Collaborative edge computing (CEC) is an emerging paradigm enabling sharing of the coupled data, computation, and networking resources among heterogeneous geo-distributed edge node…
EaaS: A Service-Oriented Edge Computing Framework Towards Distributed Intelligence
Mingjin Zhang, Jiannong Cao, Yuvraj Sahni +3
Edge computing has become a popular paradigm where services and applications are deployed at the network edge closer to the data sources. It provides applications with outstanding…
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI
Lei Yang, Yanyan Lu, Jiannong Cao +2
Traditionally, AI models are trained on the central cloud with data collected from end devices. This leads to high communication cost, long response time and privacy concerns. Rece…