Customized Federated Learning for accelerated edge computing with heterogeneous task targets

Hui Jiang; Min Liu; Bo Yang; Qingxiang Liu; Jizhong Li; Xiaobing Guo

doi:10.1016/j.comnet.2020.107569

ScienceGate Book Chapters

JOURNAL ARTICLE

Customized Federated Learning for accelerated edge computing with heterogeneous task targets

Hui Jiang Min Liu Bo Yang Qingxiang Liu Jizhong Li Xiaobing Guo

Year: 2020 Journal: Computer Networks Vol: 183 Pages: 107569-107569 Publisher: Elsevier BV

DOI: 10.1016/j.comnet.2020.107569

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Enhanced Data Rates for GSM Evolution Server Edge computing Edge device Task (project management) Variance (accounting) Process (computing) Distributed computing Latency (audio) Minification Artificial intelligence Computer network Cloud computing

Metrics

Cited By

3.08

FWCI (Field Weighted Citation Impact)

Refs

0.92

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Age of Information Optimization

Physical Sciences → Computer Science → Computer Networks and Communications

Customized Federated Learning for accelerated edge computing with heterogeneous task targets

Metrics

Citation History

Topics

Related Documents

Federated Deep Learning for Heterogeneous Edge Computing

Federated learning-based approach for heterogeneous task scheduling in edge computing environments

Accelerating Decentralized Federated Learning in Heterogeneous Edge Computing

Secure federated learning with configurable reliability in heterogeneous edge computing

BOSE: Block-Wise Federated Learning in Heterogeneous Edge Computing