Blockchain-Enabled Federated Learning with Prototype Verification for Tamper-Resistant Distributed Model Training
Keywords:
blockchain, federated learning, prototype verification, tamper resistance, distributed systems, model integrity, adversarial robustness, governanceAbstract
The convergence of federated learning and blockchain technology offers a promising pathway toward tamper-resistant distributed model training, yet existing approaches often overlook the semantic integrity of learned representations. This paper presents a comprehensive framework that integrates blockchain-based immutable audit trails with prototype verification mechanisms to detect and mitigate malicious model updates in federated learning environments. Unlike conventional aggregation schemes that depend solely on statistical anomaly detection, prototype verification leverages class-level feature representations to validate the semantic consistency of contributed gradients before they are committed to the global model. The blockchain layer provides a decentralized, non-repudiable ledger of verification outcomes and model states, enabling transparent governance and post-hoc forensic analysis. We examine the structural trade-offs between verification granularity, computational overhead, and communication efficiency, and discuss how prototype anchors can be securely maintained across distributed nodes without a central authority. The system architecture is analyzed from the perspectives of scalability, adversarial robustness, and cross-silo deployment in regulated domains such as healthcare and finance. Furthermore, we explore the policy implications of embedding verifiable semantic constraints into distributed learning pipelines, including the tension between privacy preservation and auditability. This paper contributes a system-level design rationale that bridges cryptographic integrity, representation learning, and socio-technical governance, offering a blueprint for trustworthy federated learning in high-stakes applications.
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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.
