Adaptive Knowledge Discovery Using Federated Machine Learning: A Multi-Layer Architecture for Organizational Intelligence Systems

Abstract

Background: In the contemporary knowledge economy, organizations face the dual challenge of managing rapidly expanding data repositories while ensuring that actionable intelligence is generated efficiently and securely. Conventional centralized machine learning frameworks impose significant computational burdens and raise critical data governance concerns, particularly when sensitive organizational knowledge must be protected across distributed departments or enterprise units. Problem Statement: Existing knowledge discovery systems fail to adequately integrate multi-source heterogeneous data while simultaneously ensuring privacy-preserving learning, resulting in suboptimal knowledge efficiency and limited generalizability. Proposed System: This paper introduces the Adaptive Federated Knowledge Discovery System (AFKDS), a multi-layer architecture combining federated learning protocols with dynamic knowledge graph construction and ontology-driven metadata enrichment. Methodology: The system was evaluated using a synthetic dataset of 750 organizational knowledge artifacts collected from five simulated enterprise environments. Gradient aggregation was performed using a modified FedAvg algorithm enhanced with differential privacy noise injection. Key Findings: AFKDS achieved a knowledge retrieval accuracy of 91.3%, a precision of 88.7%, and a recall of 90.2%, outperforming centralized baseline models by 9.4% in accuracy and reducing average processing latency by 37%. Applications: The proposed architecture is applicable in healthcare knowledge management, financial intelligence systems, and government information repositories.