A Comprehensive Review of Federated Learning Architectures for Insider Threat Detection in Distributed SQL-Based Enterprise Environments

Insider threats remain one of the most challenging cybersecurity concerns for enterprise environments, particularly in distributed systems where sensitive data is stored and processed using SQL-based infrastructures. Conventional centralized detection methods often fail to scale securely across multi-tenant architectures, leading to privacy violations, delayed response times, and limited contextual awareness. This review explores the integration of federated learning (FL) frameworks for insider threat detection in SQL-based distributed enterprise settings. It evaluates the effectiveness of FL in maintaining data locality while training shared threat models collaboratively, thereby mitigating data exfiltration risks and privacy breaches. We analyze existing federated learning architectures—cross-device, cross-silo, and hierarchical FL—focusing on their suitability, scalability, security guarantees, and resource constraints in enterprise-grade SQL ecosystems. Furthermore, the paper identifies challenges related to data heterogeneity, model poisoning, latency, and differential privacy enforcement, and discusses emerging solutions such as blockchain integration and secure aggregation protocols. The study provides critical insights and design considerations for deploying privacy-preserving, decentralized threat detection systems in real-world enterprise contexts.