Decentralized Machine Learning Governance: Overview, Opportunities, and Challenges

Abstract

Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology.In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

“…However, on one hand, the collection of heterogeneous data from all involved nodes of the continuum might increase the pre-processing load, and on the other hand, centralized ML training might jeopardize latency requirements in critical applications. Therefore, as will also be Future Internet 2023, 15, 383 3 of 27 described in Section 2, the support of distributed and decentralized ML approaches is a key concept in IEC systems [16][17][18].…”

“…will also be described in Section 2, the support of distributed and decentralized ML approaches is a key concept in IEC systems [16][17][18].…”