Trust-Centric Federated Learning Using Blockchain-Enabled Smart Contracts: An Analysis of Data Privacy, Model Integrity, and Decentralized AI Governance
2026· International Journal of Latest Technology in Engineering, Management & Applied Science· 0 citations
TL;DR
A trust-based federated learning framework in which a smart contract enabled by blockchain oversees client registration, model update logging, hash-based integrity verification, trust score calculation, malicious node penalization, aggregation approval, and decentralised audit logging is proposed.
Abstract
Federated Learning (FL) allows multiple data possessors to collaboratively train a shared model without transferring any raw data outside their local administration limits. However, conventional server orchestrated FL is vulnerable to trusted-server reliance, black-box contribution auditability, malicious model updates, and weak accountability. This article proposes a trust-based federated learning framework in which a smart contract enabled by blockchain oversees client registration, model update logging, hash-based integrity verification, trust score calculation, malicious node penalization, aggregation approval, and decentralised audit logging.
The paper uses a clearly labelled simulated experimental setup involving 16 clients having a non-IID Fashion-MNIST classification task. Three of these 16 clients are malicious and submit either poisoned updates or updates having hash mismatches. The proposed trust-centric blockchain federated learning configuration is compared with traditional federated averaging based on parameters such as model accuracy, verification rate, malicious-update identification, F1-score, transaction cost, and governance traceability. Based on the simulation results, the new framework enhances the global accuracy from 79.6% to 86.8% under the attack setting. Further hash-verification coverage is complete for submitted updates. Likewise, the simulation rejects the three malicious submissions in the simulated round. Lastly, the provenance records become immutable via incurring additional latency of 121.3 ms per update. The paper contributes a technically defined, IEEE-style framework for federated learning, smart-contract trust scoring, model-integrity verification, and decentralized AI governance. The outcomes are not offered as evidence of real-world deployment but as a reproducible academic design and evaluation framework for future empirical real-world deployment.
Collaborative threat intelligence sharing has become essential for defending distributed enterprise and smart city infrastructures against increasingly sophisticated cyber threats. However, organizations remain reluctant to share raw security data due to privacy, regulatory, and trust concerns. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without exposing local data. Nevertheless, traditional FL-based intrusion detection frameworks remain vulnerable to model poisoning, Byzantine attacks, and lack transparent accountability mechanisms for cross-organization collaboration. This paper proposes an engineering framework for privacy-preserving federated threat intelligence sharing that integrates trust-aware robust aggregation with blockchain-based integrity anchoring. The proposed architecture introduces a dynamic reputation mechanism that evaluates participant reliability across communication rounds and assigns adaptive aggregation weights to mitigate malicious updates. To enhance transparency and non-repudiation, model update hashes and trust evolution records are anchored on a permissioned blockchain through smart contracts, ensuring immutable auditability without exposing sensitive parameters. The framework is evaluated using a non-IID partition of the ToN-IoT dataset across multiple simulated organizations. Experimental results demonstrate significant robustness improvements under adversarial environments. Under Byzantine attacks with 20% malicious clients, the proposed trust-aware aggregation mechanism achieves approximately 95% Accuracy and 95% F1-macro, compared with approximately 89% obtained using conventional FedAvg. Furthermore, under highly adversarial conditions involving 40% malicious participants, the proposed framework maintains approximately 95% Accuracy and F1-macro, whereas FedAvg degrades to approximately 78% Accuracy and 77% F1-macro. These results confirm the effectiveness of the proposed trust-aware aggregation mechanism in mitigating malicious updates while preserving stable convergence and reliable intrusion detection performance.
Mehdi Houichi, Faouzi Jaidi, Adel Bouhoula· Journal of King Saud Univers...· 0 citations
This research reveals that federated learning by blockchain is a robust and scalable platform to enable privacy-preserving artificial intelligence in healthcare, finance, IoT, smart city, and industrial applications.
Arthi D, R. Anand, Palaniappan Sambandam et al.· International journal of com...· 0 citations
A blockchain-based federated learning framework based on quality auditing, fairness deviation, and Sybil-resistant similarity (FedQFS) is proposed, which achieves over 95.5% accuracy on the MNIST dataset and maintains strong robustness under Sybil attacks scenarios.
Tian Fang· Poster Volume 0008 The 2026...· 0 citations
Federated Learning (FL) enables collaborative model training across decentralized participants without sharing raw data. However, existing FL systems remain vulnerable to Byzantine attacks and suffer from a lack of accountability, verifiability, and economic incentives for honest participation. We present BFL-Guard, a novel blockchain-orchestrated federated learning framework integrating: (i) zk-SNARK-based zero-knowledge gradient proofs, (ii) an on-chain Byzantine-tolerant aggregation smart contract, and (iii) a tokenized incentive protocol (FedToken). BFL-Guard stores model checkpoints as IPFS hashes anchored on Ethereum, ensuring tamper-evident auditability. Experiments on CIFAR-10 and Shakespeare benchmarks demonstrate 95.2% and 87.6% accuracy in IID and Non-IID settings, surpassing all baselines while converging 12.4% faster even under 30% Byzantine injection.
Kumaresan S, Thirumal L, E. V et al.· International Journal of Lat...· 0 citations
The proposed framework incorporates shard-based transaction processing, Byzantine fault tolerant (PBFT) consensus, resilient federated aggregation, and AES-GCM-encrypted model updates and demonstrated higher throughput and lower authentication latency under the evaluated workloads.
Federated Learning (FL) enables distributed machine learning without sharing raw data, but its reliance on a central aggregation server introduces critical vulnerabilities gradient inversion attacks, Byzantine poisoning, free-riding by rational participants, and single-point-of-failure risk. Blockchain has been proposed as a structural remedy, giving rise to the field of Blockchain-Enabled Federated Learning (BEFL). reviews exactly eight representative peer-reviewed BEFL systems published selected to cover four core challenge dimensions: privacy, security, scalability, and incentive design. compare each system across accuracy under data heterogeneity, formal privacy guarantees, Byzantine robustness, throughput, and communication efficiency. find that every reviewed system excels on one or two dimensions while underperforming on others, and that no single published system simultaneously resolves all four challenges. Based on this review identify four fundamental research gaps and conclude with a structured research agenda. This study also reveals that the base FL optimizer contributes more to model accuracy than any blockchain or privacy mechanism a finding with significant design implications.
Raman Dubey, A. Jain, Richa Sharma· International Conference on...· 0 citations
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