FedSemGNN: A scalable, semantic-aware federated RL framework for efficient 6G edge orchestration

Sixth-generation (6G) edge networks must orchestrate heterogeneous services across massive device populations under strict latency, energy, and privacy constraints. Existing reinforcement-learning (RL) approaches for edge orchestration either ignore semantic task content, lack awareness of network topology, or incur prohibitive communication overhead when deployed in federated settings, limiting applicability to latency-critical 6G scenarios. To address these shortcomings, we propose FedSemGNN, a hierarchical federated RL framework that jointly integrates three complementary capabilities: (i) continual semantic task embeddings regularized by elastic weight consolidation for intent-aware service placement, (ii) graph convolutional network-based topology encoding that captures spatial relationships among edge nodes for structurally coherent decisions, and (iii) a two-level proximal policy optimization architecture with priority-aware scheduling and adaptive semantic thresholds tailored to diverse 6G service classes. Comprehensive evaluation on the EdgeSimPy simulator over 1000 orchestration steps against five representative baselines demonstrates that FedSemGNN achieves 39.08 ms orchestration latency (3.3× faster than flat federation), near-perfect semantic fidelity, and 21× lower communication overhead (0.72 MB). Scalability experiments spanning seven network sizes from 6 to 1000 nodes (a 167× range) confirm that fidelity is preserved and computation time grows near-linearly. These results position FedSemGNN as a control-plane foundation for privacy-preserving, semantic-aware orchestration in large-scale 6G edge deployments.