Blockchain-Integrated Trust Management for Backdoor-Resistant Distributed Large Language Model Ecosystems
Keywords:
blockchain, trust management, backdoor resistance, large language model, distributed ecosystem, smart contract, decentralized governance, provenance, differential privacy, vertical split learningAbstract
The rapid proliferation of large language models across decentralized infrastructures has introduced unprecedented challenges in ensuring model integrity, trustworthiness, and resilience against adversarial manipulations. Backdoor attacks, wherein malicious actors embed hidden triggers that cause targeted misbehavior, pose a particularly insidious threat in distributed ecosystems where multiple stakeholders contribute data, compute, or model updates. This paper proposes a blockchain-integrated trust management framework designed specifically for backdoor-resistant distributed large language model ecosystems. The framework leverages immutable ledger properties, smart contract-based governance, and decentralized identity to establish verifiable provenance for training data, model checkpoints, and inference outputs. We examine architectural trade-offs between transparency, latency, and scalability, and discuss how consensus mechanisms can be adapted to support heterogeneous participants with varying trust levels. A hybrid on-chain and off-chain verification strategy is introduced to reconcile the computational overhead of blockchain operations with the high-throughput requirements of large language model deployment. The paper further addresses backdoor resistance through a combination of cryptographic attestation, differential privacy, and prototype-based anomaly detection, drawing on recent advances in vertical split learning defense. Governance mechanisms such as staking, slashing, and reputation scoring are analyzed in the context of aligning incentives for honest behavior. Deployment considerations including energy consumption, regulatory compliance, and cross-chain interoperability are critically assessed. By synthesizing insights from distributed systems, cryptoeconomics, and machine learning security, this work provides a comprehensive blueprint for building trustworthy and resilient large language model ecosystems that are resistant to backdoor attacks while maintaining operational efficiency and fairness.
References
1. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
2. Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., ... & Zhang, C. (2019). Towards federated learning at scale: System design. Proceedings of Machine Learning and Systems, 1, 374-388.
3. Li, Y., Zhou, Y., & Chen, C. (2022). Blockchain-based federated learning with decentralized trust. IEEE Transactions on Network and Service Management, 19(4), 4526-4540.
4. Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2021). DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 18(5), 2046-2060.
5. Gu, T., Dolan-Gavitt, B., & Garg, S. (2017). BadNets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733.
6. Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2020). How to backdoor federated learning. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2938-2948.
7. Cheng, K., Fan, L., & Yang, Q. (2022). Split learning backdoor attacks and defenses. Proceedings of the 31st International Joint Conference on Artificial Intelligence, 2997-3004.
8. Shui, Y., Jin, R., Dou, Z., & Gao, Z. (2026). ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning. arXiv preprint arXiv:2604.03595.
9. Buterin, V. (2016). Ethereum: A next-generation smart contract and decentralized application platform.
10. Gao, Y., Zhang, Y., & Liu, Y. (2021). zk-SNARKs in machine learning: A survey. ACM Computing Surveys, 54(6), 1-35.
11. Tian, F. (2017). An agri-food supply chain traceability system for China based on RFID & blockchain technology. 2016 13th International Conference on Service Systems and Service Management, 1-6.
12. Hardjono, T., & Smith, N. (2021). Decentralized identity and verifiable credentials for IoT. IEEE Internet of Things Journal, 8(13), 10522-10535.
13. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273-1282.
14. Cheng, H., Yu, Y., & Wang, X. (2023). A survey on blockchain-based machine learning: Architecture, methods, and applications. Journal of Systems Architecture, 137, 102850.
15. Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018). Federated learning with non-IID data. arXiv preprint arXiv:1806.00582.
16. Zhang, L., Xu, J., & Lin, F. (2022). Smart contract security: A survey. IEEE Access, 10, 26217-26241.
17. Kiayias, A., Russell, A., David, B., & Oliynykov, R. (2017). Ouroboros: A provably secure proof-of-stake blockchain protocol. Proceedings of the 37th Annual International Cryptology Conference, 357-388.
18. Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., ... & Yellick, J. (2018). Hyperledger Fabric: A distributed operating system for permissioned blockchains. Proceedings of the 13th EuroSys Conference, 1-15.
19. Jiang, Y., Li, J., & Wang, C. (2023). Trusted execution environments for secure federated learning: A survey. IEEE Transactions on Dependable and Secure Computing, 20(5), 3980-3997.
20. Xie, C., Huang, K., Chen, P. Y., & Li, B. (2020). DBA: Distributed backdoor attacks against federated learning. International Conference on Learning Representations.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
