Communication-Efficient Federated Reinforcement Learning for Edge Offloading in AGI-MEC Systems

Air–ground integrated mobile edge computing (AGI-MEC) integrates aerial and terrestrial resources to provide efficient computing services for massive mobile terminals (MTs), enabling 6 G network intelligence. However, jointly optimizing offloading and power control remains challenging due to dynamic channels, resource constraints, communication overhead, and privacy concerns. To address these issues, this paper proposes a clustering-based two-layer federated deep reinforcement learning (CTL-FDRL) algorithm. A clustered federated training framework with parameter sharing is first developed, in which raw data exchange is replaced by periodic model aggregation, enabling MTs to learn offloading policies locally while preserving privacy. Furthermore, an efficient representative MT selection method is introduced. A self-organizing map (SOM)-based MT clustering method is designed to adaptively group MTs without a predefined number of clusters. Guided by the derived convergence bound, representative selection within each cluster is posed as a linear programming problem. The resulting representatives are used for federated aggregation, which reduces communication overhead without degrading model performance. Simulation results verify the superiority of CTL-FDRL, achieving about 28.7% higher cumulative reward, 31.5% lower delay, and 18.3% lower energy consumption compared with baseline algorithms.