CoFT: A Fair and Transparent Compensation Framework for Hierarchical Federated Learning
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cryptography and Data Security · Privacy-Preserving Technologies in Data · Blockchain Technology Applications and Security
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Blockchain Technology R&D Laboratory, Department of Game Engineering, Dong-Eui University, Busan 47340, Republic of Korea
Division of Computer Engineering and Artificial Intelligence, Pukyong National University, Busan 48513, Republic of Korea
The Industrial Science Technology Research Institute, Pukyong National University, Busan 48513, Republic of Korea
Funding
’Technology Commercialization Collaboration Platform Construction’ project of the INNOPOLIS FOUNDATION · IITP (Institute of Information & Communications Technology Planning & Evaluation)-ITRC
References (30)
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