# One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction

arXiv: https://arxiv.org/abs/1911.01682 · HTML: https://mlsanity.com/paper/1911.01682/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Similarity-based Label Inference Attack against Training and Inference of Split Learning](https://mlsanity.com/paper/2203.05222/) (2022)
- [Split Without a Leak: Reducing Privacy Leakage in Split Learning](https://mlsanity.com/paper/2308.15783/) (2023)
- [An Efficient Privacy-aware Split Learning Framework for Satellite Communications](https://mlsanity.com/paper/2409.08538/) (2024)
- [Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation](https://mlsanity.com/paper/2410.09125/) (2024)
- [RockNet: Distributed Learning on Ultra-Low-Power Devices](https://mlsanity.com/paper/2510.13320/) (2025)
- [Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments](https://mlsanity.com/paper/2310.18479/) (2023)

## Abstract (arXiv)

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.