Work

Toward Cost-Efficient Online Transfer Learning in Distributed Cloud-Edge Networks

Year: 2025

Type: article

Source: IEEE Transactions on Networking

DOI: 10.1109/ton.2025.3597200

Authors: Konglin Zhu, Fei Wang, Lei Jiao, Yulan Yuan, Xiaojun Lin, Lin Zhang

Institutions: Beijing University of Posts and Telecommunications, University of Oregon, Hong Kong University of Science and Technology, The Hong Kong University of Science and Technology (Guangzhou), Purdue University West Lafayette

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: closed

Funders: National Science Foundation, Beijing Innovation Center for Future Chip, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities

Awards: Collaborative Research: CNS Core: Small: Edge AI with Streaming Data: Algorithmic Foundations for Online Learning and Control, G4450737046, CAREER: Orchestrating Edge Infrastructures and Mobile Devices under Uncertainty to Provision Edge AI as a Service, G6283812490, CNS Core: Small: Integrating Real-Time Learning and Control for Large and Dynamic Networked Computer Systems, Collaborative Research: CNS Core: Small: Edge AI with Streaming Data: Algorithmic Foundations for Online Learning and Control

OpenAlex ID: W4413278221

FWCI:

Cites: 28

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Related to: 10