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[2602.16109] Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes
Summary
The paper presents FedGraph-AGI, a federated learning framework designed to enhance cross-border insider threat detection in government financial schemes by integrating AGI reasoning with graph neural networks.
Why It Matters
This research addresses the critical challenge of sharing sensitive intelligence across jurisdictions while maintaining privacy. By combining advanced AI techniques, it offers a promising solution to improve security in government financial systems, which is increasingly vital in a globalized economy.
Key Takeaways
- FedGraph-AGI achieves 92.3% accuracy in detecting insider threats, outperforming existing methods.
- The framework preserves data privacy while enabling effective intelligence sharing across borders.
- AGI reasoning contributes significantly to performance improvements in threat detection.
- The system is scalable, efficiently handling data from over 50 clients.
- This research represents a novel integration of AGI with federated learning in cybersecurity.
Computer Science > Cryptography and Security arXiv:2602.16109 (cs) [Submitted on 18 Feb 2026] Title:Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes Authors:Srikumar Nayak, James Walmesley View a PDF of the paper titled Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes, by Srikumar Nayak and 1 other authors View PDF HTML (experimental) Abstract:Cross-border insider threats pose a critical challenge to government financial schemes, particularly when dealing with distributed, privacy-sensitive data across multiple jurisdictions. Existing approaches face fundamental limitations: they cannot effectively share intelligence across borders due to privacy constraints, lack reasoning capabilities to understand complex multi-step attack patterns, and fail to capture intricate graph-structured relationships in financial networks. We introduce FedGraph-AGI, a novel federated learning framework integrating Artificial General Intelligence (AGI) reasoning with graph neural networks for privacy-preserving cross-border insider threat detection. Our approach combines: (1) federated graph neural networks preserving data sovereignty; (2) Mixture-of-Experts (MoE) aggregation for heterogeneous jurisdictions; and (3) AGI-powered reasoning via Large Action Models (LAM) performing causal inference over graph data. Through experiments on a 50,000-transaction dataset across 10 jurisdictions, FedGraph-AGI a...
