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Federated Large Domain Model System
Rong, Chunming Seo, Jungwon Zhao, Zihan Catak, Ferhat Ozgur Geng, Jiahui Jaatun, Martin Gilje
Rong, Chunming
Seo, Jungwon
Zhao, Zihan
Catak, Ferhat Ozgur
Geng, Jiahui
Jaatun, Martin Gilje
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
As organizations increasingly seek to build Foundation Models (FMs) using their own proprietary data, many are adopting private and in-house cloud infrastructures (often in addition to public clouds) to address concerns over cost, data privacy, and data sovereignty. However, these isolated private clouds frequently lack interoperability, creating barriers to cross-institutional collaboration, which is vital for training robust Domain-Specific Foundation Models (DSFMs) that rely on large and diverse datasets. Additionally, underutilized resources in private clouds lead to significant global energy inefficiencies. In this paper, we propose the Federated Large Domain Model System (FLDMS), a conceptual framework designed to facilitate collaborative foundation model development across multiple private cloud environments. We review the necessary enabling technologies, including decentralized protocols for data privacy and Large Language Models (LLMs) for automated orchestration, and present a high-level system design demonstrating how these components can be integrated. By enabling secure and efficient cross-organization cooperation, FLDMS provides a blueprint for building DSFMs while addressing the inefficiencies inherent in siloed private cloud systems.
Citation
C. Rong, J. Seo, Z. Zhao, F. O. Catak, J. Geng, and M. G. Jaatun, “Federated Large Domain Model System,” Blockchain: Research and Applications, vol. 6, no. 3, p. 100277, Sep. 2025, doi: 10.1016/J.BCRA.2025.100277
Source
Blockchain: Research and Applications
Conference
Keywords
ActivityPub, Blockchain, Cloud computing, Foundation model, Large language model
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Source
Publisher
Elsevier