Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction

By Yusuke Koda, Jihong Park, Medhi Bennis, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura, Kota Nakashima. 2020

72 citations recorded by the source corpus.

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 <italic>multimodal split learning (MultSL)</italic>, 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 16<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> yields 16<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> lower communication latency and 2.8% less privacy leakage compared to the case without compression.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

NoPeek: Information leakage reduction to share activations in distributed deep learning (2020, background): “Split learning has been adopted in Internet of Things (IOT) and edge-device machine learning settings in [20, 21, 22, 23] including multi-modal fusion based machine learning across edge devices in [24, 25] with data collected at the edge on imagery and millimeter wave (mmWave) radio frequency (RF)…”
FedML: A Research Library and Benchmark for Federated Machine Learning (2020, methodology): “Applications of this model to wireless edge devices are described in [127, 128].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “While in [80], SL was considered for received power prediction for mmWave systems in a privacy-”
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, methodology): “Such techniques include blockchain-based data administration, secure management and auditing methods, collaborative infrastructure, decentralized management architecture, and blockchain-based optimization [146].”