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 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 regarding their suitability for use cases is often difficult. This work presents a CDML system conceptualization and CDML archetypes to support comparison of CDML systems and introduce scientific and practical audiences to the principal functioning and key traits of CDML systems.

翻译：各类协作式分布式机器学习（CDML）系统，包括联邦学习系统和群体学习系统，具有不同的关键特征，旨在以保护机密性的方式利用资源开发和运用机器学习模型。为满足应用场景需求，需选择合适的CDML系统。然而，不同CDML系统在适用性方面的比较常存在困难。本文提出了一种CDML系统概念化框架及CDML原型，以支持系统间的比较，并帮助科学界和实务界了解CDML系统的基本运行原理与关键特征。