Efficient Split Learning for Collaborative Intelligence in Next-generation Mobile Networks

With the emergence of communication systems and deep learning techniques, the native intelligence has been envisioned as a primary power of future networks. In this work, we investigate the schemes of distributed communication-computation integrated networks and propose a split learning based solution for multi-gNB intelligence, abbreviated as MgC-SL. By carrying out a data-model split mechanism, MgC-SL mitigates the computation requirements of each node and enables more gNBs to participate the collaborative learning tasks. The simulation results verify that such distributed scheme significantly saves the communication and computation costs without the degradation of the task performance. A joint indicator is also formulated for performance analysis. Combining the proposed schemes and the corresponding indicator, some insights and guides for the system designs can be obtained to improve the efficiency of the next-generation network intelligence.