A Privacy-Preserved Split Learning Solution for Deep Learning-Based mmWave Beam Selection

Abstract

Side information, like light detection and ranging data, is promising to help the millimeter wave (mmWave) system achieve efficient link configuration through machine learning methods. However, collecting and using this information may violate user privacy. In this letter, we propose a novel privacy-preserved split learning (SL) solution for the beam selection problem, in which the raw data is not uploaded during training and inference. In particular, it uses the proposed feature mix method to get better generalization performance and robustness to non-independently identically distribution (non-iid) data. Extensive experiments demonstrate that the proposed method outperforms learning-based baselines (e.g. the original SL and federated learning) in a variety of settings.