Personalized Federated Learning on NLOS Acoustic Signal Classification

In the process of identifying non-line-of-sight (NLOS), acoustics-based indoor positioning needs to collect audio recordings of sound fields in multiple rooms and upload them to the central server for training. Once the transmission process and server-side suffer malicious attacks, private data will also be leaked. To solve the training difficulty and privacy issues at the same time, we propose a novel Personalized Federated Learning (PFL) model combined with user frequency and room data capacity, taking into account the significant differences in positioning data with room layout. The proposed model can accurately identify the differences between different room data when aggregating on the server-side. By collecting data in the actual indoor environment and comparing the existing algorithms, the accuracy of the proposed method in the data verification of unfamiliar rooms is 90%.