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