Federated Fingerprint Localization Method Based on Adaptive Aggregation and Feature Alignment

Wi-Fi-based fingerprint localization plays a crucial role in indoor localization services, where the collection of fingerprint localization information by mobile clients poses privacy risks during data transmission. Recently, Federated Learning (FL) has been employed for training fingerprint localization models without data sharing. However, the non-independent identical distribution characteristics of Wi-Fi fingerprint data result in suboptimal performance of the aggregation model in FL. In this letter, we propose a transformer-based Coef-Feature Alignment Federated Learning (TCFAFed) method to enhance FL performance. An adaptive aggregation approach is devised to dynamically obtain the optimal model and address client drift issues. The coefficient of variation is utilized to improve the calculation accuracy of the aggregation coefficient, while feature alignment constraints are imposed to restrict local feature representation. Extensive experiments demonstrate that our proposed method achieves superior global localization accuracy compared to classical FL aggregation methods.