Collaborative Distributed Machine Learning

TL;DR

This paper introduces a conceptual framework and archetypes for collaborative distributed machine learning systems, aiding comparison and understanding of their key traits for different use cases.

Contribution

It provides a novel conceptualization and archetypes for CDML systems, facilitating better comparison and understanding of their functionalities and suitability.

Findings

Developed a CDML system conceptualization

Defined CDML archetypes based on key traits

Facilitated comparison of CDML systems for use cases

Abstract

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.