Year: 2025
Type: article
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: Applied Sciences
DOI: 10.3390/app152312568
Authors: Siwan Noh, Sang Uk Shin, Kyung-Hyune Rhee
Institutions: Dong-Eui University, Pukyong National University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: INNOPOLIS Foundation
Award: G1949382561
OpenAlex ID: W4416765365