Research Article
Open Access
TrustCompute: Result Validation, Reputation-Aware Scheduling and Quality-Based Payments for Outsourced LoRA Fine-Tuning
1
Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
* Corresponding Author:
Kuo-Hui Yeh,
[email protected]
Volume 2, Issue 3
2026
Received: 09 July 2026
Revised: 14 September 2026
Accepted: 16 September 2026
Published: 19 September 2026
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CC BY 4.0 License
Article Information
Published in
Journal of Reliable and Secure Computing
Volume/Issue
Volume 2, Issue 3, 2026
Pages
179-193
Abstract
Outsourced model fine-tuning requires reliable task allocation, result validation, and incentives for quality. We present TrustCompute, a compute-sharing platform integrating two-stage adapter verification, reputation-aware scheduling, quality-based payments, and auditable ledger records for LoRA fine-tuning. Our evaluation combines synthetic workloads, real GPU-based fine-tuning, and a local Ethereum Virtual Machine (EVM) environment. Across 440 validation cases, all 400 invalid submissions were rejected and all 40 valid submissions were accepted. With 50% unreliable workers, reputation-aware scheduling achieved 100% task completion and reduced the mean failed-attempt time by 89% relative to round-robin scheduling. An integration experiment completed 120 real fine-tuning tasks. Additional simulations showed that identity resets can exploit exploration and that quality proportional payments can improve strategic workers’ delivered quality. These findings support jointly designing verification, scheduling, and payment mechanisms. TrustCompute verifies compliance with result acceptance criteria, but does not prove adherence to a prescribed training procedure.
Graphical Abstract
Keywords
outsourced computing
LoRA fine-tuning
result validation
reputation-aware scheduling
quality-based payment
auditable ledger
Data Availability Statement
The data used to support the findings of this study are available from the corresponding author upon request.
Funding
This work was supported without any funding.
Conflicts of Interest
Kuo-Hui Yeh served as an Editor-in-Chief of the Journal of Reliable and Secure Computing at the time of manuscript submission. To ensure the integrity of the peer-review process, Kuo-Hui Yeh was not involved in the editorial handling, peer review, or decision-making process for this manuscript, which was handled independently by another editor. The remaining authors declare no conflicts of interest.
AI Use Statement
The authors declare that Claude Opus 5, developed by Anthropic, was used for drafting and translation during the preparation of the manuscript. The authors have carefully reviewed, revised, and verified the AI-assisted output and take full responsibility for the content of the manuscript.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Lee, L. F., Chang, Y. Y., & Yeh, K. H. (2026). TrustCompute: Result Validation, Reputation-Aware Scheduling and Quality-Based Payments for Outsourced LoRA Fine-Tuning. Journal of Reliable and Secure Computing, 2(3), 179-193. https://doi.org/10.62762/JRSC.2026.867713
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TY - JOUR AU - Lee, Lin-Fa AU - Chang, Yi-Yu AU - Yeh, Kuo-Hui PY - 2026 DA - 2026/09/19 TI - TrustCompute: Result Validation, Reputation-Aware Scheduling and Quality-Based Payments for Outsourced LoRA Fine-Tuning JO - Journal of Reliable and Secure Computing T2 - Journal of Reliable and Secure Computing JF - Journal of Reliable and Secure Computing VL - 2 IS - 3 SP - 179 EP - 193 DO - 10.62762/JRSC.2026.867713 UR - https://www.icck.org/article/abs/JRSC.2026.867713 KW - outsourced computing KW - LoRA fine-tuning KW - result validation KW - reputation-aware scheduling KW - quality-based payment KW - auditable ledger AB - Outsourced model fine-tuning requires reliable task allocation, result validation, and incentives for quality. We present TrustCompute, a compute-sharing platform integrating two-stage adapter verification, reputation-aware scheduling, quality-based payments, and auditable ledger records for LoRA fine-tuning. Our evaluation combines synthetic workloads, real GPU-based fine-tuning, and a local Ethereum Virtual Machine (EVM) environment. Across 440 validation cases, all 400 invalid submissions were rejected and all 40 valid submissions were accepted. With 50% unreliable workers, reputation-aware scheduling achieved 100% task completion and reduced the mean failed-attempt time by 89% relative to round-robin scheduling. An integration experiment completed 120 real fine-tuning tasks. Additional simulations showed that identity resets can exploit exploration and that quality proportional payments can improve strategic workers’ delivered quality. These findings support jointly designing verification, scheduling, and payment mechanisms. TrustCompute verifies compliance with result acceptance criteria, but does not prove adherence to a prescribed training procedure. SN - 3070-6424 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Lee2026TrustCompu,
author = {Lin-Fa Lee and Yi-Yu Chang and Kuo-Hui Yeh},
title = {TrustCompute: Result Validation, Reputation-Aware Scheduling and Quality-Based Payments for Outsourced LoRA Fine-Tuning},
journal = {Journal of Reliable and Secure Computing},
year = {2026},
volume = {2},
number = {3},
pages = {179-193},
doi = {10.62762/JRSC.2026.867713},
url = {https://www.icck.org/article/abs/JRSC.2026.867713},
abstract = {Outsourced model fine-tuning requires reliable task allocation, result validation, and incentives for quality. We present TrustCompute, a compute-sharing platform integrating two-stage adapter verification, reputation-aware scheduling, quality-based payments, and auditable ledger records for LoRA fine-tuning. Our evaluation combines synthetic workloads, real GPU-based fine-tuning, and a local Ethereum Virtual Machine (EVM) environment. Across 440 validation cases, all 400 invalid submissions were rejected and all 40 valid submissions were accepted. With 50\% unreliable workers, reputation-aware scheduling achieved 100\% task completion and reduced the mean failed-attempt time by 89\% relative to round-robin scheduling. An integration experiment completed 120 real fine-tuning tasks. Additional simulations showed that identity resets can exploit exploration and that quality proportional payments can improve strategic workers’ delivered quality. These findings support jointly designing verification, scheduling, and payment mechanisms. TrustCompute verifies compliance with result acceptance criteria, but does not prove adherence to a prescribed training procedure.},
keywords = {outsourced computing, LoRA fine-tuning, result validation, reputation-aware scheduling, quality-based payment, auditable ledger},
issn = {3070-6424},
publisher = {Institute of Central Computation and Knowledge}
}
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