SafeTwinNet: Secure and Adaptive Learning Ecosystems for Digital Twins

Digital Twin (DT) technology is emerging as a critical infrastructure for data-driven modeling and intelligent decision-making across diverse domains. However, training models collaboratively across multiple DT nodes introduces several challenges, such as transmitting large-scale sensitive data, meeting real-time latency constraints, and ensuring system security. These challenges call for an integrated ecosystem that combines collaborative learning and adaptive networking to fully harness DT-generated intelligence. Motivated by these challenges, we propose SafeTwinNet, a learning ecosystem that bridges intelligent learning and adaptive networking. SafeTwinNet integrates federated learning (FL) to enable privacy-preserving collaborative training, and AI-native networking (AINN) to ensure secure and adaptive communication among distributed DT nodes. This synergy between learning and networking intelligence forms a trustworthy foundation for large-scale DT-driven services and lays the groundwork for next-generation intelligent infrastructures.