28 citations · 64 across the 5 of their papers we have counts for
6 papers
ACME: Adaptive Customization of Large Models via Distributed Systems
Ziming Dai, Chao Qiu, Fei Gao +2
Pre-trained Transformer-based large models have revolutionized personal virtual assistants, but their deployment in cloud environments faces challenges related to data privacy and…
EdgeMatrix: A Resource-Redefined Scheduling Framework for SLA-Guaranteed Multi-Tier Edge-Cloud Computing Systems
Shihao Shen, Yuanming Ren, Yanli Ju +3
With the development of networking technology, the computing system has evolved towards the multi-tier paradigm gradually. However, challenges, such as multi-resource heterogeneity…
When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework
Xinjing Yuan, Lingjun Pu, Lei Jiao +3
In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the…
Collaborative Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Network
Shihao Shen, Yiwen Han, Xiaofei Wang +2
Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edge-cloud n…
Building Resilient Web 3.0 with Quantum Information Technologies and Blockchain: An Ambilateral View
Xiaoxu Ren, Minrui Xu, Dusit Niyato +4
Web 3.0 pursues the establishment of decentralized ecosystems based on blockchain technologies to drive the digital transformation of physical commerce and governance. Through cons…
When Quantum Information Technologies Meet Blockchain in Web 3.0
Minrui Xu, Xiaoxu Ren, Dusit Niyato +5
With the drive to create a decentralized digital economy, Web 3.0 has become a cornerstone of digital transformation, developed on the basis of computing-force networking, distribu…