The management of network intelligence in Beyond 5G (B5G) networks encompasses the complex challenges of scalability, dynamicity, interoperability, privacy, and security. These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence (AI)-based analytics, empowering seamless integration across the entire Continuum (Edge, Fog, Core, Cloud). This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning (FL)-based anomaly detection in B5G networks. By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm, which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize normal behavior, the system orchestrates network intelligence to detect and prevent cyber-attacks. Integrated into a B5G Zero-touch Service Management (ZSM) aligned Security Framework, the proposal utilizes multi-domain and multi-tenant orchestration to automate and scale the deployment of FL-agents and AI-based anomaly detectors, enhancing reaction capabilities against cyber-attacks. The proposed FL architecture can be dynamically deployed across the B5G Continuum, utilizing a hierarchy of Network Intelligence orchestrators for real-time anomaly and security threat handling. Implementation includes FL enforcement operations for interoperability and extensibility, enabling dynamic deployment, configuration, and reconfiguration on demand. Performance validation of the proposed solution was conducted through dynamic orchestration, FL, and real-time anomaly detection processes using a practical test environment. Analysis of key performance metrics, leveraging the 5G-NIDD dataset, demonstrates the system’s capability for automatic and near real-time handling of anomalies and attacks, including real-time network monitoring and countermeasure implementation for mitigation.