Decoupling Neural Networks to Leverage Uniform Representation and Balance Personalization and Collaboration in Federated Learning

IF 8.9 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE transactions on neural networks and learning systems Pub Date : 2025-07-29 DOI:10.1109/tnnls.2025.3586600

Zhangmin Huang, Pengcheng Wang, Shaojie Tang, Bo Lyu, Lingfang Zeng

引用次数: 0

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解耦神经网络在联邦学习中利用统一表示和平衡个性化与协作

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

CiteScore

23.80

自引率

9.60%

发文量

2102

审稿时长

3-8 weeks

期刊介绍： The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.