# Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction

arXiv: https://arxiv.org/abs/2003.00645 · HTML: https://mlsanity.com/paper/2003.00645/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 18 of 18)

- [Similarity-based Label Inference Attack against Training and Inference of Split Learning](https://mlsanity.com/paper/2203.05222/) (2022)
- [Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation](https://mlsanity.com/paper/2106.00999/) (2021)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks](https://mlsanity.com/paper/2104.13629/) (2021)
- [Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks](https://mlsanity.com/paper/2112.09407/) (2021)
- [SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks](https://mlsanity.com/paper/2203.14094/) (2022)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [Protecting Split Learning by Potential Energy Loss](https://mlsanity.com/paper/2210.09617/) (2022)
- [ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation](https://mlsanity.com/paper/2304.11396/) (2023)
- [UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environments](https://mlsanity.com/paper/2407.02693/) (2024)
- [VOMTC: Vision Objects for Millimeter and Terahertz Communications](https://mlsanity.com/paper/2409.09330/) (2024)
- [Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices](https://mlsanity.com/paper/2403.09141/) (2024)
- [Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation](https://mlsanity.com/paper/2410.09125/) (2024)
- [Split Learning in Computer Vision for Semantic Segmentation Delay Minimization](https://mlsanity.com/paper/2412.14272/) (2024)
- [Edge AI Collaborative Learning: Bayesian Approaches to Uncertainty Estimation](https://mlsanity.com/paper/2410.08651/) (2024)
- [Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images](https://mlsanity.com/paper/2501.18618/) (2025)
- [Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems](https://mlsanity.com/paper/2604.06914/) (2026)
- [A Machine Learning Framework for Distributed Functional Compression over Wireless Channels in IoT](https://mlsanity.com/paper/2201.09483/) (2022)

## Abstract (arXiv)

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.