本地长尾数据的动态自适应联合学习

IF 5.5 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Services Computing Pub Date : 2024-10-11 DOI:10.1109/tsc.2024.3478796

Juncheng Pu, Xiaodong Fu, Hai Dong, Pengcheng Zhang, Li Liu

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Dynamic Adaptive Federated Learning on Local Long-Tailed Data

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来源期刊

IEEE Transactions on Services Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

11.50

自引率

6.20%

发文量

278

审稿时长

>12 weeks

期刊介绍： IEEE Transactions on Services Computing encompasses the computing and software aspects of the science and technology of services innovation research and development. It places emphasis on algorithmic, mathematical, statistical, and computational methods central to services computing. Topics covered include Service Oriented Architecture, Web Services, Business Process Integration, Solution Performance Management, and Services Operations and Management. The transactions address mathematical foundations, security, privacy, agreement, contract, discovery, negotiation, collaboration, and quality of service for web services. It also covers areas like composite web service creation, business and scientific applications, standards, utility models, business process modeling, integration, collaboration, and more in the realm of Services Computing.