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The goal of this study is to improve the accuracy of millimeter wave receivedpower prediction by utilizing camera images and radio frequency (RF) signals,while gathering image inputs in a communication-efficient andprivacy-preserving manner. To this end, we propose a distributed multimodalmachine learning (ML) framework, coined multimodal split learning (MultSL), inwhich a large neural network (NN) is split into two wirelessly connectedsegments. The upper segment combines images and received powers for futurereceived power prediction, whereas the lower segment extracts features fromcamera images and compresses its output to reduce communication costs andprivacy leakage. Experimental evaluation corroborates that MultSL achieveshigher accuracy than the baselines utilizing either images or RF signals.Remarkably, without compromising accuracy, compressing the lower segment outputby 16x yields 16x lower communication latency and 2.8% less privacy leakagecompared to the case without compression.

Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction