  Focusing on the received power prediction of millimeter-wave (mmWave)
radio-frequency (RF) signals, we propose a multimodal split learning (SL)
framework that integrates RF received signal powers and depth-images observed
by physically separated entities. To improve its communication efficiency while
preserving data privacy, we propose an SL neural network architecture that
compresses the communication payload, i.e., images. Compared to a baseline
solely utilizing RF signals, numerical results show that SL integrating only
one pixel image with RF signals achieves higher prediction accuracy while
maximizing both communication efficiency and privacy guarantees.
