Design of an integrated federated large language models for semantic reasoning and self-organizing 6G networks

Traditional optimization methods are ineffective in contexts where interference patterns, device density, and mobility dynamics change rapidly for future 6G networks. This work introduces a federated orchestration system that combines big language models into the control loop for semantic interpretation and causally based decision-making. The system generates constraint-aware policy drawings, structures semantic graphs from raw telemetry, and assesses candidate topology alterations using a reasoning-augmented optimization engine. Before deployment, a probabilistic verification module guarantees that each intervention fulfills tight performance requirements, and a continuous counterfactual evaluation process turns successful decisions into lightweight edge controllers. Simulations of dense urban, event-driven, and emergency mobility situations show reduced signaling overhead, delay, and instability and good service-level compliance. The results show that semantic reasoning and federated optimization can create interpretable, adaptive, and trustworthy self-organizing 6G infrastructures in process. Simulations suggest 40–55% telemetry compression, 18–30% latency reduction in congested regimes, and verifiable SLA compliance above 0.99 probability. Beyond metrics, the architecture offers interpretability, federated privacy preservation, and long-horizon adaptability sets. The result is a pathway toward 6G infrastructures that are not only faster, but context-aware, cautious, and continuously improving in process.