协作式分布机器学习

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2024-11-20 DOI:10.1145/3704807

David Jin, Niclas Kannengießer, Sascha Rank, Ali Sunyaev

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引用次数: 0

摘要

为了以保密方式利用资源开发和使用机器学习（ML）模型，开发了各种具有不同关键特征的协作分布式机器学习（CDML）系统，包括联合学习系统和群学习系统。为满足用例要求，需要选择合适的 CDML 系统。然而，对 CDML 系统进行比较以评估其是否适合用例往往很困难。为了支持对 CDML 系统进行比较，并向科学界和实际受众介绍 CDML 系统的主要功能和关键特征，这项工作提出了 CDML 系统概念化和 CDML 原型。

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Collaborative Distributed Machine Learning

Various collaborative distributed machine learning (CDML) systems, including federated learning systems and swarm learning systems, with different key traits were developed to leverage resources for the development and use of machine learning (ML) models in a confidentiality-preserving way. To meet use case requirements, suitable CDML systems need to be selected. However, comparison between CDML systems to assess their suitability for use cases is often difficult. To support comparison of CDML systems and introduce scientific and practical audiences to the principal functioning and key traits of CDML systems, this work presents a CDML system conceptualization and CDML archetypes.

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.