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

arXiv: https://arxiv.org/abs/2602.16109 · HTML: https://mlsanity.com/paper/2602.16109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 23 of 23)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Subgraph Federated Learning with Missing Neighbor Generation](https://mlsanity.com/paper/2106.13430/) (2021)
- [Stochastic Controlled Averaging for Federated Learning with Communication Compression](https://mlsanity.com/paper/2308.08165/) (2023)
- [The AGI Containment Problem](https://mlsanity.com/paper/1604.00545/) (2016)
- [FedGraph: Federated Graph Learning with Intelligent Sampling](https://mlsanity.com/paper/2111.01370/) (2021)
- [Scalable Graph Learning for Anti-Money Laundering: A First Look](https://mlsanity.com/paper/1812.00076/) (2018)
- [Alleviating Structural Distribution Shift in Graph Anomaly Detection](https://mlsanity.com/paper/2401.14155/) (2024)
- [Large Action Models: From Inception to Implementation](https://mlsanity.com/paper/2412.10047/) (2024)

## Abstract (arXiv)

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.