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- W4297784488 abstract "Fingerprint-based localization plays an important role in indoor location-based services, where the position information is usually collected in distributed clients and gathered in a centralized server. However, the overloaded transmission as well as the potential risk of divulging private information burdens the application.Owning the ability to address these challenges, federated learning (FL)-based fingerprinting localization comes into people's sights, which aims to train a global model while keeping raw data locally. However, in distributed machine learning (ML) scenarios, the unavoidable database heterogeneity usually degrades the performance of existing FL-based localization algorithm (FedLoc). In this paper, we first characterize the database heterogeneity with a computable metric, i.e., the area of convex hull, and verify it by experimental results. Then, a novel heterogeneous FL-based localization algorithm with the area of convex hull-based aggregation (FedLoc-AC) is proposed. Extensive experimental results, including real-word cases are conducted. We can conclude that the proposed FedLoc-AC can achieve an obvious prediction gain compared to FedLoc in heterogeneous scenarios and has almost the same prediction error with it in homogeneous scenarios. Moreover, the extension of FedLoc-AC in multi-floor cases is proposed and verified." @default.
- W4297784488 created "2022-10-01" @default.
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- W4297784488 date "2022-03-29" @default.
- W4297784488 modified "2023-09-27" @default.
- W4297784488 title "Federated Learning-Based Localization with Heterogeneous Fingerprint Database" @default.
- W4297784488 doi "https://doi.org/10.48550/arxiv.2203.15388" @default.
- W4297784488 hasPublicationYear "2022" @default.
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