HPFL: Federated Learning by Fusing Multiple Sensor Modalities with Heterogeneous Privacy Sensitivity Levels

Abstract

Solving classification problems to understand multi-modality sensor data has become popular, but rich-media sensors, e.g., RGB cameras and microphones, are privacy-invasive. Though existing Federated Learning (FL) algorithms allow clients to keep their sensor data private, they suffer from degraded performance, particularly lower classification accuracy and longer training time, than centralized learning. We propose a Heterogeneous Privacy Federated Learning (HPFL) paradigm to capitalize on the information in the privacy insensitive data (such as mmWave point clouds) while keeping the privacy sensitive data (such as RGB images) private because sensor data are of diverse sensitivity levels. We evaluate the HPFL paradigm on two representative classification problems: semantic segmentation and emotion recognition. Extensive experiments demonstrate that the HPFL paradigm outperforms: (i) the popular FedAvg by 18.20% in foreground accuracy (semantic segmentation) and 4.20% in F1-score (emotion recognition) under non-i.i.d. sample distributions and (ii) the state-of-the-art FL algorithms by 12.40%-17.70% in foreground accuracy and 2.54%-4.10% in F1-score.