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To leverage data for the sufficient training of machine learning (ML) modelsfrom multiple parties in a confidentiality-preserving way, variouscollaborative distributed ML (CDML) system designs have been developed, forexample, to perform assisted learning, federated learning, and split learning.CDML system designs show different traits, including high agent autonomy, MLmodel confidentiality, and fault tolerance. Facing a wide variety of CDMLsystem designs with different traits, it is difficult for developers to designCDML systems with traits that match use case requirements in a targeted way.However, inappropriate CDML system designs may result in CDML systems failingtheir envisioned purposes. We developed a CDML design toolbox that can guidethe development of CDML systems. Based on the CDML design toolbox, we presentCDML system archetypes with distinct key traits that can support the design ofCDML systems to meet use case requirements.

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