Blockchain-Enabled Edge Intelligence: Architectures, Consensus, and Security in Future Networks
Blockchain , Federated Learning, Edge Intelligence , Security , network
As the architecture of next-gen computing paradigms transitions away from centralized cloud infrastructures toward distributed Edge Intelligence (EI) and Federated Learning (FL) frameworks, severe vulnerabilities, including single points of failure, data poisoning, the absence of trust among distributed nodes, and no incentive mechanisms for contributing nodes have been created. Blockchain technology has the potential to provide a secure cryptographic basis for ensuring the integrity of the data, decentralized model orchestration, and facilitating trust less cooperation. The purpose of this paper is to provide a detailed systematic survey of the merger between blockchain architecture and edge-native intelligence. We construct a multi-dimensional taxonomy to track the evolution of blockchain frameworks from monolithic to lightweight, resource-optimized edge variations. We also analyze how new consensus mechanisms reduce the fundamental conflicts between computational overhead and transaction throughput. Subsequently, we investigate the mutualistic integration model by mapping how blockchain secures the FL pipeline and how ML optimizes blockchain. Finally, we explore new applications and highlight significant open research challenges (i.e., the scalability-decentralization-security trilemma) while discussing future research directions that will help researchers implement secure, scalable, and decentralized edge intelligence ecosystems.
"Blockchain-Enabled Edge Intelligence: Architectures, Consensus, and Security in Future Networks", JETNR - JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (www.JETNR.org), ISSN:2984-9276, Vol.4, Issue 6, page no.b127-b131, June-2026, Available :https://rjpn.org/JETNR/papers/JETNR2606138.pdf
Volume 4
Issue 6,
June-2026
Pages : b127-b131
Paper Reg. ID: JETNR_235160
Published Paper Id: JETNR2606138
Downloads: 00061
Research Area: Science and Technology
Country: Theni, Tamilnadu, India
ISSN: 2984-9276 | IMPACT FACTOR: 9.87 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.87 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: RJPN (IJPublication) Janvi Wave
