Collaborative Distributed Machine Learning
This work addresses a practical problem for researchers and practitioners in selecting appropriate distributed ML systems, but it is incremental as it focuses on conceptualization rather than new methods or data.
The paper tackles the difficulty of comparing collaborative distributed machine learning systems for use case suitability by presenting a conceptualization and archetypes to support such comparisons.
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.