A Design Toolbox for the Development of Collaborative Distributed Machine Learning Systems

To leverage training data for the sufficient training of ML models from multiple parties in a confidentiality-preserving way, various collaborative distributed machine learning (CDML) system designs have been developed, for example, to perform assisted learning, federated learning, and split learning. CDML system designs show different traits, for example, high agent autonomy, machine learning (ML) model confidentiality, and fault tolerance. Facing a wide variety of CDML system designs with different traits, it is difficult for developers to design CDML systems with traits that match use case requirements in a targeted way. However, inappropriate CDML system designs may result in CDML systems failing their envisioned purposes. We developed a CDML design toolbox that can guide the development of CDML systems. Based on the CDML design toolbox, we present CDML system archetypes with distinct key traits that can support the design of CDML systems to meet use case requirements.