Lefeng Zhang

MA-HGAT is extended into a cross-layer multi-agent heterogeneous graph attention framework that models contracts, firmware artifacts, device fleets, and transaction streams with a unified four-role, nine-relation schema and provides a unified and deployable framework for detecting logic vulnerabilities across the contract and device layers of blockchain-enabled IoT systems.

Dataset distillation, intended to compress data while preserving model performance, actually leaks sensitive information about the original training data and model architecture.