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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM48442

Decentralizing AI Computing: A Study with IPFS and Public Peer-to-Peer Networks

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM48442

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM48442

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MGV2DR3GPSDCG

M.J Guru Venkatesh 2) Dharun R 3) Guru Prathosh. S4) Dr. C. Gnanaprakasam

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Abstract

Ipfs and public peer-to-peer (P2P) networks were adopted to make AI calculations more decentralized. Taking AI workloads over a decentralized network could bring better fault tolerance, guarantee data safety, and increased privacy. This research explores the challenges of centralized AI, such as data confidentiality, scalability, and accessibility, while discussing the promise of decentralized AI. Combining IPFS with decentralized systems improves scalability, data protection, and fault tolerance.

KEYWORDS

Decentralized AI IPFS Peer-to-peer networks Data privacy AI computation.

Cite This Article

M.J Guru Venkatesh 2) Dharun R 3) Guru Prathosh. S4) Dr. C. Gnanaprakasam, "Decentralizing AI Computing: A Study with IPFS and Public Peer-to-Peer Networks," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/IJSREM48442

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Publication Date

5/21/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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