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 letter, 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.

中文翻译：

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使用异构指纹数据库的基于联邦学习的本地化

基于指纹的定位在室内定位服务中发挥着重要作用，其中位置信息通常收集在分布式客户端中并收集在集中式服务器中。然而，超载的传输以及泄露私人信息的潜在风险给应用程序带来了负担。拥有应对这些挑战的能力，基于联邦学习（FL）的指纹定位进入人们的视野，旨在训练全局模型，同时将原始数据保留在本地。然而，在分布式机器学习 (ML) 场景中，不可避免的数据库异构性通常会降低现有基于 FL 的定位算法 (FedLoc) 的性能。在这封信中，我们首先用一个可计算的度量来表征数据库的异质性，即凸包面积，并通过实验结果验证。然后，提出了一种基于凸包聚合区域的基于异构 FL 的定位算法（FedLoc-AC）。进行了广泛的实验结果，包括真实案例。我们可以得出结论，在异构场景中，与 FedLoc 相比，所提出的 FedLoc-AC 可以实现明显的预测增益。