Year: 2026
Type: article
Abstract: The dominance of centralized artificial intelligence architectures raises significant concerns regarding privacy, data ownership, and control. These limitations have motivated the development of decentralized learning paradigms that aim to remove reliance on a central authority during model training. While federated learning represents an intermediate step by allowing distributed training without raw data exchange, it still depends on a centralized server which could lead to single-point vulnerabilities. Beyond this, a fully decentralized learning in general faces challenges in security vulnerabilities, absence of governance, and lack of incentive alignment. Recent advances in blockchain technology offer a promising foundation for addressing these issues. This paper provides a systematic analysis of blockchain’s mechanism-level roles in security, consensus, smart contract, and incentives to support decentralized learning. By reviewing state-of-the-art approaches, this paper suggests that appropriately designed blockchain architectures have the potential to enable practical, secure, and incentive-compatible decentralized learning as technological capabilities continue to evolve.
Source: Future Internet
DOI: 10.3390/fi18020098
Authors: Daren Wang, Tengfei Ma, Juntao Zhu, Haihan Duan
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7128818367