Federated Graph Neural Network for Real-Time Distributed Monitoring System Against Illicit Blockchain Transaction Accounts

The anonymity and decentralization of blockchain facilitate illicit blockchain transactions, while the dispersed computing resources in IoT(Internet of Things) scenarios pose challenges like insufficient high-dimensional feature processing capability, poor unknown anomaly detection, and high model update costs for identifying illicit accounts. To address these issues, we propose FLTG-Double GAT, a dedicated monitoring method for decentralized resource scenarios. It aggregating multi-party knowledge to enable cross-organizational detection of illicit transaction, while protecting data privacy by only uploading detection model parameter. It adopt a customized feature extraction paradigm combining double GAT and PCA balances deep feature mining and efficiency, enabling high-precision modeling on edge devices. At the same time, a GRU-driven real-time update mechanism is used. This realizes lightweight adaptation of the detection model to dynamic transactions and enhances early warning capability. Experiments on the Elliptic++ dataset show it achieves 99.80% detection recall and 99.28% prediction recall with low computational complexity, verifying its scalability and practical value in large-scale distributed blockchain monitoring systems.