  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.
