  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.
