Various collaborative distributed machine learning (CDML) systems, including federated learning systems and swarm learning systems, with diferent key traits were developed to leverage resources for the development and use of machine learning(ML) models in a conidentiality-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 diicult. To support comparison of CDML systems and introduce scientiic and practical audiences to the principal functioning and key traits of CDML systems, this work presents a CDML system conceptualization and CDML archetypes.

翻译：为在保护隐私的前提下利用资源开发和使用机器学习模型，已发展出多种具有不同关键特征的协同分布式机器学习系统，包括联邦学习系统和群体学习系统。为满足用例需求，需要选择合适的协同分布式机器学习系统。然而，评估不同协同分布式机器学习系统对特定用例的适用性往往较为困难。为支持协同分布式机器学习系统的比较，并向科研与实践领域的读者介绍协同分布式机器学习系统的基本工作原理与关键特征，本研究提出了协同分布式机器学习系统的概念化框架与原型分类体系。