Collaborative Distributed Machine Learning

Abstract

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.