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Hongyi Liu

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Review Aug 2026

Blockchain Empowered Trustworthy Agent Networks: Foundations, Taxonomy, and Future Directions

AI agents are evolving from isolated task executors into networked autonomous entities that can communicate, delegate tasks, invoke tools, access external knowledge, and participate in cross-platform service and economic workflows. This evolution gives rise to open agent networks, where heterogeneous agents owned by different stakeholders interact without naturally shared infrastructures for identity, authorization, auditability, reputation, or settlement. This survey and tutorial article reviews the literature over the period 1980--2026 on the evolution from classical multi-agent systems to open agent networks, with a particular focus on LLM-based autonomous agents, agent interoperability protocols, Internet-of-Agents infrastructures, and blockchain-enabled trust mechanisms. We first review this evolution and show how the trust boundary expands from individual execution to cross-agent, cross-platform, and cross-organizational interaction. We then identify a network-level trust crisis that cannot be fully addressed by single-agent safety mechanisms or closed multi-agent coordination techniques, and develop a five-dimensional taxonomy covering entity and capability trust, authorization and delegation trust, information and provenance trust, coordination and group-robustness trust, and accountability and settlement trust. Based on this taxonomy, we examine how blockchain can provide shared identity, verifiable authorization, tamper-evident provenance, auditable collaboration, incentive alignment, and value settlement for trustworthy agent networks. We further synthesize the mapping between agent-network risks, trust requirements, and blockchain-enabled mechanisms, and clarify the role of blockchain as a shared trust layer rather than a replacement for agent security, semantic verification, privacy protection, or robust reasoning.

Liehuang Zhu, Yuhang Li, Tianxing Wang et al. · 0 citations
#edge computing Sep 2026

Blockchain-Assisted Privacy-Preserving and Robust Federated Learning in Edge Computing

Federated learning (FL) enables distributed clients to collaboratively train machine learning models without sharing raw data, making it well-suited for edge computing environments. However, deploying FL at the edge introduces critical challenges, including potential model poisoning by malicious clients or edge servers, increased privacy risks, and communication bottlenecks. To address these issues, we propose a two-layer blockchain-assisted FL framework that ensures privacy, robustness, and efficiency. In our design, each client uploads an encrypted local update to an edge server for privacy-preserving validation, while a lightweight, sparse upload strategy is adopted to reduce communication overhead. We design a two-layer defense strategy that employs the secure cosine similarity technique and a truth discovery algorithm to resist Byzantine attacks from both malicious clients and edge servers, while a consortium blockchain maintained by edge servers guarantees decentralized and tamper-resistant global model updates. Theoretical analysis and extensive experiments under both IID and non-IID data partitions demonstrate that the proposed scheme effectively defends against targeted and untargeted poisoning attacks and preserves the privacy of local model updates. It also substantially reduces communication overhead. These results indicate that our framework provides a practical and scalable solution for privacy-preserving and robust FL in edge environments.

Xiaodong Shen, Chang Xu, Liehuang Zhu et al. · 0 citations