AI-Driven Federated Knowledge Graph Architecture for Real-Time Cyber Threat Intelligence Optimization in Smart Government Information Systems (Scientific Journal of Research Studies in Future Computer Sciences, 2025)
The rapid digital transformation of governmental infrastructures has significantly increased the complexity, scale, and interconnectedness of cyber ecosystems within smart government environments. Contemporary public-sector information systems increasingly rely on cloud computing, edge intelligence, Internet of Things infrastructures, distributed digital services, and cross-organizational data exchange platforms to support critical administrative and public operations. Although these technologies improve operational efficiency and citizen-oriented service delivery, they simultaneously introduce highly dynamic cybersecurity vulnerabilities that cannot be efficiently addressed through conventional centralized security architectures. Existing cyber threat intelligence systems frequently suffer from fragmented data structures, delayed threat correlation mechanisms, limited interoperability, insufficient contextual reasoning, and privacy-related constraints in multi-agency environments. These limitations reduce the capability of government institutions to detect and mitigate sophisticated cyberattacks in real time. This study proposes an AI-driven federated knowledge graph architecture designed to optimize real-time cyber threat intelligence operations within smart government information systems. The proposed framework integrates federated learning mechanisms, graph-based semantic threat representation, distributed artificial intelligence models, and edge-enabled cybersecurity analytics into a unified intelligent architecture. The model enables decentralized cyber threat learning across multiple governmental nodes without requiring direct exchange of sensitive organizational data. Simultaneously, the knowledge graph layer facilitates semantic correlation, threat reasoning, attack path analysis, and contextual cyber intelligence extraction across heterogeneous data environments. The proposed architecture incorporates real-time intrusion analysis, adaptive threat classification, federated anomaly detection, and graph-based cyber relationship inference to improve cybersecurity situational awareness and response efficiency. The methodological framework utilizes contemporary cybersecurity datasets, distributed learning principles, and smart infrastructure protection mechanisms to evaluate system performance under large-scale governmental network conditions. Analytical findings demonstrate that the integration of federated artificial intelligence and knowledge graph reasoning substantially improves detection accuracy, threat correlation speed, interoperability, and privacy preservation compared with conventional centralized cyber intelligence approaches. The study contributes a scalable and intelligent cybersecurity framework capable of supporting resilient digital governance infrastructures under continuously evolving cyber threat conditions. The proposed architecture also provides strategic implications for future smart government cybersecurity policies, distributed digital trust management, and AI-enabled cyber defense ecosystems.