Federated Learning for Agentic Gen AI in Financial Risk Management for National Financial Security

Agentic Gen AI deployment is critically hampered by the siloed and sensitive nature of financial data, stringent data privacy regulations (e.g., GDPR, CCPA), and growing cybersecurity threats. This paper provides a comprehensive analysis of the synergistic integration of Federated Learning with Generative and Agentic AI systems for financial risk management. We explore the technical foundations of FL, its role in training and deploying Gen AI models like Large Language Models (LLMs) for synthetic data generation and risk analysis, and its function as the backbone for secure, collaborative Agentic AI systems that can autonomously navigate complex, multi-institutional workflows. The paper surveys key applications in anti-financial crime (AFC), credit risk assessment, and market risk modeling, while also addressing the persistent challenges—including communication overhead, systems heterogeneity, and model security—that must be overcome. We summarize recent FL frameworks including FedAvg with partial model averaging, federated LLM fine-tuning with differential privacy, secure multi-party computation protocols, and edge-FL hybrid systems. Our technical review include: (1) FedF1 aggregation for imbalanced financial datasets achieving 10-15% AUC improvement, (2) Privacy-preserving synthetic data generation via federated diffusion models with 0.85-.95 data fidelity, (3) Agentic AI systems with federated policy learning demonstrating 80-90\% task completion rates, and (4) Secure aggregation protocols providing formal privacy guarantees. Experimental results across financial applications show significant performance gains: 20-30% improvement in AML detection, 20-25% reduction in false positives, and 30-40% cost savings in automated compliance. The reviewed architectures address critical challenges in data privacy, regulatory compliance (GDPR, CCPA, Basel III), and cross-institutional collaboration while maintaining model accuracy within 2-4% of centralized approaches. Our work establishes FL as the foundational infrastructure for next-generation AI systems in finance, enabling secure collaboration across data silos without compromising sensitive information. All results are from cited literature.