Ai-Based Meta-Orchestration for Fl-Based Anomaly Detection in B5g Networks

G networks will need to handle Multi-domain, multi-tenant and multi-operator scenarios where the security orchestration of services and network functions will face high complexity of scalability, interoperability and security. In particular, 6G networks will need to manage AI-based network functions to support analytics that can be encapsulated as virtual functions that need to be dynamically orchestrated, configured, deployed, decommissioned, migrated and reconfigured on demand. The distributed nature of these scenarios requires a federated learning FL approach to handle AI-based collaborative learning where FL-agents can be deployed in the continuum of any network segment of the network. This paper proposes an AI-based meta-orchestration approach for multi-domain and multi-tenant 6 G deployments intended to choreograph the FLbased AI functions. The meta-Orchestration encompasses diverse phases, including selection of the orchestration strategy, the orchestration graph building and the selection of best AI-based orchestration algorithm depending on the actual context, thereby maximizing the effectiveness of the allocation of the FL-agents. The implementation and performance evaluation to orchestrate FL agents with diverse AI-based algorithms across the continuum proves our approach to detect anomalies using FL in distributed scenarios.