Work

Personalized federated learning on NLOS acoustic signal classification

Year: 2023

Type: article

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.

Source: Electronics Letters

DOI: 10.1049/ell2.12790

Authors: Hucheng Wang, Suo Qiu, Jingjing Wang, Lei Zhang, Zhi Wang, Xiaonan Luo

Institutions: Guilin University of Electronic Technology, Zhejiang University, Kyungpook National University, Chang'an University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,540

Funder: National Natural Science Foundation of China