Personalized federated learning on NLOS acoustic signal classification

Abstract

To identify non-line-of-sight, acoustics-based indoor positioning needs to collect audio recordings of sound fields in multiple rooms and upload them to the central server for training. However, if the transmission process or the server-side suffers from malicious attacks, private data may be leaked. To address both training difficulties and privacy issues simultaneously, a novel model for personalized federated learning is proposed. This model incorporates user frequency and room data capacity while also taking into account significant differences in positioning data and room layout. The proposed model can accurately identify differences between various room data during aggregation on the server-side. By collecting data in actual indoor environments and comparing it with existing algorithms, the proposed method achieved 90% accuracy in verifying data for unfamiliar rooms.