Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

Srikumar Nayak, James Walmesley

TL;DR

FedGraph-AGI is introduced, a novel federated learning framework integrating Artificial General Intelligence (AGI) reasoning with graph neural networks for privacy-preserving cross-border insider threat detection, opening new directions for privacy-preserving cross-border intelligence sharing.

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 achieves 92.3% accuracy, significantly outperforming federated baselines (86.1%) and centralized approaches (84.7%). Our ablation studies reveal AGI reasoning contributes 6.8% improvement, while MoE adds 4.4%. The system maintains epsilon = 1.0 differential privacy while achieving near-optimal performance and scales efficiently to 50+ clients. This represents the first integration of AGI reasoning with federated graph learning for insider threat detection, opening new directions for privacy-preserving cross-border intelligence sharing.

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

TL;DR

FedGraph-AGI is introduced, a novel federated learning framework integrating Artificial General Intelligence (AGI) reasoning with graph neural networks for privacy-preserving cross-border insider threat detection, opening new directions for privacy-preserving cross-border intelligence sharing.

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 achieves 92.3% accuracy, significantly outperforming federated baselines (86.1%) and centralized approaches (84.7%). Our ablation studies reveal AGI reasoning contributes 6.8% improvement, while MoE adds 4.4%. The system maintains epsilon = 1.0 differential privacy while achieving near-optimal performance and scales efficiently to 50+ clients. This represents the first integration of AGI reasoning with federated graph learning for insider threat detection, opening new directions for privacy-preserving cross-border intelligence sharing.
Paper Structure (59 sections, 3 theorems, 21 equations, 1 figure, 3 tables, 2 algorithms)

This paper contains 59 sections, 3 theorems, 21 equations, 1 figure, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. The Need for AGI-Enhanced Federated Graph Learning
  4. Limitations of Current State-of-the-Art Methods
  5. Our Contributions
  6. Organization
  7. Related Work
  8. Federated Learning for Privacy-Preserving Collaboration
  9. Graph Neural Networks and Federated Extensions
  10. Insider Threat Detection in Financial Systems
  11. Artificial General Intelligence and Large Action Models
  12. Mixture-of-Experts Architectures
  13. Gap Identification
  14. Methodology
  15. Problem Formulation
  16. ...and 44 more sections

Key Result

Theorem 1

Under assumptions: (1) local losses $\mathcal{L}_k$ are $L$-smooth and $\mu$-strongly convex; (2) gradient variance bounded: $\mathbb{E}[\|\nabla\mathcal{L}_k(\theta)\|^2] \leq G^2$; (3) local models do not drift too far: $\mathbb{E}[\|\theta_k - \theta_{global}\|^2] \leq \Delta^2$; FedGraph-AGI wit where $\theta^*$ is the optimal global model.

Figures (1)

  • Figure 1: FedGraph-AGI System Architecture. Local GNN clients process jurisdiction-specific graph data, Mixture-of-Experts layer aggregates updates handling heterogeneity, and AGI reasoning module performs causal inference and threat analysis.

Theorems & Definitions (5)

  • Theorem 1: Convergence of FedGraph-AGI
  • proof : Proof Sketch
  • Theorem 2: Differential Privacy of FedGraph-AGI
  • proof : Proof Sketch
  • Proposition 1: Computational Complexity