FEDERATED AI AGENTS FOR COLLABORATIVE DECISION ANALYTICS ACROSS RETAIL, BANKING, AND NETWORK INFRASTRUCTURES

The practical deployment of distributed intelligent systems across heterogeneous organizational environments faces significant challenges due to data privacy constraints, domain heterogeneity, and the limited reasoning capability of conventional federated learning frameworks. To address these challenges, this paper proposes a Federated Agentic Intelligence Framework (FAIF) that integrates Federated Learning (FL), Large Language Models (LLMs), Multi-Agent Systems (MAS), Knowledge Graphs (KGs), Retrieval-Augmented Generation (RAG), and Explainable Artificial Intelligence (XAI) to enable privacy-preserving and autonomous collaborative analytics. First, FAIF employs a federated learning architecture to support distributed model training without exposing sensitive organizational data, thereby preserving data confidentiality while enabling collaborative intelligence. Second, domain-specific autonomous agents are designed to perform specialized analytical tasks, including supply chain coordination and inventory synchronization in retail, fraud detection and risk assessment in finance, and autonomous network management, anomaly detection, and traffic optimization in networking environments. Third, retrieval-augmented generation combined with knowledge graphs enhances contextual reasoning, knowledge integration, and explainability, while explainable AI modules improve the transparency and trustworthiness of agent decisions. Finally, a coordinated multi-agent collaboration mechanism enables adaptive decision-making, efficient knowledge sharing, and scalable cross-domain intelligence. The proposed FAIF provides a robust, scalable, and privacy-preserving framework for trustworthy distributed analytics, demonstrating strong adaptability to heterogeneous operational ecosystems and offering an effective foundation for next-generation autonomous enterprise intelligence systems.