Work

Data augmentation-based personalized federated learning for indoor localization with non-IID fingerprint data

Year: 2025

Type: article

Abstract: With the growing use of federated learning (FL) in indoor localization, model drift caused by heterogeneous fingerprint data remains a critical challenge. Existing personalized FL methods attempt to mitigate model drift by aligning model parameters, but this often results in high computational costs. In this paper, we propose DA-PFedLoc, a novel data augmentation-based personalized federated learning framework for indoor localization. DA-PFedLoc addresses fingerprint heterogeneity through two key techniques: (i) local fingerprint data augmentation to reduce distribution divergence among clients, and (ii) a contrastive loss that aligns the probability distributions of local and global models, enabling personalized training. Experimental results on two public indoor localization datasets show that DA-PFedLoc achieves a 98.07% reduction in computational overhead relative to the baselines, while simultaneously improving classification accuracy by 4.52%, and also demonstrates faster convergence and improved stability.

Source: Journal of King Saud University - Computer and Information Sciences

DOI: 10.1007/s44443-025-00370-2

Authors: Xuejun Zhang, Yexin Fan, Gong Liu, Zhuo Chen, Haiyan Huang

Institutions: Lanzhou Jiaotong University, University of Glasgow

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,990

Funder: National Natural Science Foundation of China