Asynchronous Federated Learning-Based Indoor Localization With Non-IID Data

In fingerprint-based indoor localization, the collection of clients' location data may cause serious privacy concerns. The integration of federated learning (FL) into fingerprint-based indoor localization facilitates privacy-preserving distributed training. However, the non-independently and identically distributed (Non-IID) nature of data among different clients and the heterogeneity among devices can impact the training performance of FL, leading to a reduction in localization accuracy. To address these challenges, we propose an asynchronous FL (AFL) method, named AFedLoc, for fingerprint-based indoor localization. The proposed method considers multiple factors during the model aggregation process to adjust the model weights of different clients. These factors are: 1) information weight (IW) that reflects the richness of information in local datasets; 2) area weight (AW) that mitigates the impact of Non-IID data; and 3) temporal weight (TW) that reflects the staleness of local model parameters when uploaded to the server, which can reduce the impact of device heterogeneity. The performance of the proposed AFedLoc is evaluated using publicly available indoor localization datasets. The results demonstrate that AFedLoc can effectively mitigate the impact of Non-IID data and the staleness of local models in asynchronous model aggregation.