A Procedural Architecture for Agent Trust and Credibility Verification in Distributed Ledger-Based Federated Learning
Soonduck Yoo, Dae-Yeol Kim, Do-Yup Kim · IEEE Access · 2026
Agent-based federated learning (FL) enables multiple nodes to collaboratively train a global model without sharing raw data, thereby mitigating privacy and security concerns. However, FL inherently relies on the credibility of participating agents, making trust assurance critical in environments susceptible to malicious attacks and operational faults. This paper proposes a procedural architecture for evaluating and enhancing agent credibility in distributed ledger-based FL (DLFL) systems. The proposed architecture spans the agent life cycle, consisting of the pre-training, in-training, and post-training stages, and integrates three verification domains: data-based, behavior-based, and technology-based verification. In the pre-training stage, data-based verification assesses data integrity and quality through reference distribution analysis and entropy reduction to improve learning stability. During the in-training stage, behavior-based verification establishes a dual mechanism that monitors and analyzes agents’ learning processes and outcomes and detects anomalies in updates and performance. In the post-training stage, technology-based verification ensures record immutability and accountability through blockchain, cryptographic validation, and auditing mechanisms. By combining these multi-layered procedures, the proposed architecture enables systematic and continuous evaluation of agent credibility, fostering a trustworthy FL ecosystem and enabling future applications in autonomous agent collaboration and trust-oriented AI governance.