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.

开发了一个协作分布式机器学习设计工具箱，该工具箱可以指导协作分布式机器学习系统的开发，并提供不同特征的 CDML 系统原型，以满足使用案例要求。

协作分布式机器学习系统开发的设计工具箱

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

Collaborations among various entities, such as companies, research labs, AI
agents, and edge devices, have become increasingly crucial for achieving
machine learning tasks that cannot be accomplished by a single entity alone.
This is likely due to factors such as security constraints, privacy concerns,
and limitations in computation resources. As a result, collaborative learning
(CL) research has been gaining momentum. However, a significant challenge in
practical applications of CL is how to effectively incentivize multiple
entities to collaborate before any collaboration occurs. In this study, we
propose ICL, a general framework for incentivized collaborative learning, and
provide insights into the critical issue of when and why incentives can improve
collaboration performance. Furthermore, we show the broad applicability of ICL
to specific cases in federated learning, assisted learning, and multi-armed
bandit with both theory and experimental results.

本文提出 ICL—— 一种激励协同学习的通用框架，研究为什么和何时激励能够提高合作效果，并通过理论和实验结果展示了 ICL 在联邦学习、协同辅助学习和多臂赌博机等情景下的广泛应用。