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.

翻译：跨境内部威胁对政府金融计划构成严峻挑战，尤其是在处理涉及多个司法管辖区的分布式、隐私敏感数据时。现有方法面临根本性局限：由于隐私约束无法有效跨境共享情报，缺乏理解复杂多步攻击模式的推理能力，且未能捕捉金融网络中复杂的图结构关系。我们提出FedGraph-AGI，一种新颖的联邦学习框架，它将人工通用智能推理与图神经网络相结合，用于隐私保护的跨境内部威胁检测。我们的方法整合了：（1）保护数据主权的联邦图神经网络；（2）面向异构司法管辖区的专家混合聚合机制；以及（3）通过大型行动模型在图数据上进行因果推理的AGI驱动推理。通过在涵盖10个司法管辖区的50,000笔交易数据集上进行实验，FedGraph-AGI实现了92.3%的准确率，显著优于联邦基线方法（86.1%）和集中式方法（84.7%）。我们的消融研究表明，AGI推理贡献了6.8%的性能提升，而MoE贡献了4.4%。该系统在保持ε = 1.0差分隐私的同时实现了接近最优的性能，并能高效扩展到50个以上的客户端。这代表了AGI推理与联邦图学习在内部威胁检测领域的首次融合，为隐私保护的跨境情报共享开辟了新方向。