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Volume 15
Issue 23
10.3390/app152312568
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Article

CoFT: A Fair and Transparent Compensation Framework for Hierarchical Federated Learning

by
Siwan Noh
1,
Sang Uk Shin
2,* and
Kyung-Hyune Rhee
3,*
1
Blockchain Technology R&D Laboratory, Department of Game Engineering, Dong-Eui University, Busan 47340, Republic of Korea
2
Division of Computer Engineering and Artificial Intelligence, Pukyong National University, Busan 48513, Republic of Korea
3
The Industrial Science Technology Research Institute, Pukyong National University, Busan 48513, Republic of Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(23), 12568; https://doi.org/10.3390/app152312568
Submission received: 16 October 2025 / Revised: 20 November 2025 / Accepted: 21 November 2025 / Published: 27 November 2025
(This article belongs to the Special Issue AI-Enabled Next-Generation Computing and Its Applications)
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Abstract

Hierarchical Federated Learning (HFL) requires a scalable and transparent incentive mechanism, yet existing on-chain approaches are too costly and centralized solutions lack trust. To address this, we propose CoFT, a hybrid framework that manages rewards off-chain using a hierarchy of state channels and a stablecoin while securing the system’s integrity with a single on-chain cryptographic anchor. This architecture dramatically reduces on-chain complexity from a prohibitive O(N×T) to a sustainable O(I+S+N), enabling cryptographic verifiability, automated trustless payouts, and fair burden-sharing. CoFT thus offers a robust and economically viable blueprint for the financial infrastructure of large-scale, trustworthy collaborative AI ecosystems.
Keywords: Hierarchical Federated Learning; incentive mechanism; state channels Hierarchical Federated Learning; incentive mechanism; state channels

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MDPI and ACS Style

Noh, S.; Shin, S.U.; Rhee, K.-H. CoFT: A Fair and Transparent Compensation Framework for Hierarchical Federated Learning. Appl. Sci. 2025, 15, 12568. https://doi.org/10.3390/app152312568

AMA Style

Noh S, Shin SU, Rhee K-H. CoFT: A Fair and Transparent Compensation Framework for Hierarchical Federated Learning. Applied Sciences. 2025; 15(23):12568. https://doi.org/10.3390/app152312568

Chicago/Turabian Style

Noh, Siwan, Sang Uk Shin, and Kyung-Hyune Rhee. 2025. "CoFT: A Fair and Transparent Compensation Framework for Hierarchical Federated Learning" Applied Sciences 15, no. 23: 12568. https://doi.org/10.3390/app152312568

APA Style

Noh, S., Shin, S. U., & Rhee, K.-H. (2025). CoFT: A Fair and Transparent Compensation Framework for Hierarchical Federated Learning. Applied Sciences, 15(23), 12568. https://doi.org/10.3390/app152312568

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