Blockchain-Enabled Distributed Machine Learning
A special issue of Blockchains (ISSN 2813-5288).
Deadline for manuscript submissions: 31 December 2025 | Viewed by 132
Special Issue Editors
Interests: artificial intelligence security; federated learning; applied cryptography
Special Issues, Collections and Topics in MDPI journals
Interests: network security; moving target defense; computer networks; federated learning
Special Issues, Collections and Topics in MDPI journals
Interests: system security; artificial intelligence security; network and information security
Special Issues, Collections and Topics in MDPI journals
Interests: blockchain; web 3.0; data privacy
Special Issue Information
Dear Colleagues,
Traditional machine learning methods typically rely on aggregating large datasets to a central server for model training, which raises significant concerns regarding data privacy and security. Distributed Machine Learning (DML) is an emerging paradigm that addresses the challenges associated with traditional centralized machine learning approaches, particularly in scenarios involving vast amounts of data and stringent privacy requirements. Although DML inherently enhances data privacy by keeping data localized, it also introduces new security challenges. Ensuring the security of the distributed learning process and maintaining data integrity across all participating devices are ongoing concerns in DML.
Blockchain technology, with its inherent features of immutability, transparency, and decentralization, offers promising solutions to the security challenges faced by DML. By leveraging blockchain, DML systems can enhance data security, ensure the trustworthiness of model updates, and facilitate secure collaboration among distributed nodes. Yet, the integration of these technologies also brings forth novel vulnerabilities and complexities that require rigorous research and innovative approaches.
This Special Issue will consolidate cutting-edge research that advances the field of blockchain-enabled Distributed Machine Learning. By addressing the fundamental challenges and exploring novel applications, this Special Issue will contribute to the development of secure, efficient, and decentralized AI systems that can operate at scale. We welcome submissions that offer new insights, propose innovative solutions, and push the boundaries of what is possible at the convergence of blockchain and DML.
Dr. Xiangyun Tang
Dr. Tao Zhang
Dr. Li Duan
Dr. Li Chao
Dr. Yijing Lin
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Blockchains is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- blockchain
- distributed machine learning
- secure smart contracts for distributed machine learning
- data privacy and security
- data integrity and confidentiality
- decentralized identity and authentication
- blockchain-based federated learning
- resource management in distributed machine learning
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