Prediction Based Semi-Supervised Online Personalized Federated Learning for Indoor Localization

Fingerprint-based indoor localization has drawn increasing attention with the development of deep learning. Nevertheless, it faces challenges from frequent data collection and the corresponding exposure of privacy. Federated Learning (FL) is introduced into indoor localization recently for overcoming these challenges. However, most of the current FL-based indoor localization studies only focus on the static data distributions and ignore the fact that users have their preferred localization requirements. In this paper, two typical indoor localization scenarios are considered. Clients with various localization demands and data distributions collect online unlabeled data in Scenario I. For Scenario II, only one requesting user (RU) with high mobility applies for the localization service. A Prediction based Semi-supervised Online Personalized Federated Learning (PSO-PFL) is proposed for addressing these problems in the two scenarios. Experiments are conducted based on two real-world datasets. The experiment results show that PSO-PFL achieves higher personalization accuracy than centralized training and Federated Averaging, and utilizes unlabeled data efficiently. The strategy of selecting clients via prediction achieves higher accuracy than the random selection strategy. In brief, the proposed methods protect users’ privacy via FL. And the challenges of dynamical and heterogeneous stream data in indoor localization are addressed by the proposed methods which provide better personalization localization service for users than baseline methods.