Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the coordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.

翻译：将原生AI支持集成到网络架构中是6G的核心目标。联邦学习作为一种潜在范式，能够在中央服务器协调下，促进跨异构设备的去中心化AI模型训练。然而，其在6G场景中的广泛应用面临诸多挑战，例如针对局部模型更新的恶意攻击与隐私窃听，以及中心化架构的固有问题。本文提出一种支持联邦学习的可信架构，该架构融合分布式账本技术与图神经网络，具有三大核心特征：其一，引入采用同态加密的预处理层，实现局部模型的安全聚合，从而保护个体模型隐私；其二，针对预处理层中客户端与节点间的分布式图结构特性，利用图神经网络识别异常局部模型，增强系统安全性；其三，通过分布式账本技术从候选节点中选定执行中央服务器功能的节点，实现系统去中心化。此外，分布式账本通过将数据交换记录于不可篡改的透明账本中，确保可靠数据管理。仿真实验验证了该新型架构的可行性，结果表明其在异常模型检测与全局模型准确率方面均优于相关基线方法。