The rapid growth of the Internet of Things fosters collaboration among
connected devices for tasks like indoor localization. However, existing indoor
localization solutions struggle with dynamic and harsh conditions, requiring
extensive data collection and environment-specific calibration. These factors
impede cooperation, scalability, and the utilization of prior research efforts.
To address these challenges, we propose FeMLoc, a federated meta-learning
framework for localization. FeMLoc operates in two stages: (i) collaborative
meta-training where a global meta-model is created by training on diverse
localization datasets from edge devices. (ii) Rapid adaptation for new
environments, where the pre-trained global meta-model initializes the
localization model, requiring only minimal fine-tuning with a small amount of
new data. In this paper, we provide a detailed technical overview of FeMLoc,
highlighting its unique approach to privacy-preserving meta-learning in the
context of indoor localization. Our performance evaluation demonstrates the
superiority of FeMLoc over state-of-the-art methods, enabling swift adaptation
to new indoor environments with reduced calibration effort. Specifically,
FeMLoc achieves up to 80.95% improvement in localization accuracy compared to
the conventional baseline neural network (NN) approach after only 100 gradient
steps. Alternatively, for a target accuracy of around 5m, FeMLoc achieves the
same level of accuracy up to 82.21% faster than the baseline NN approach. This
translates to FeMLoc requiring fewer training iterations, thereby significantly
reducing fingerprint data collection and calibration efforts. Moreover, FeMLoc
exhibits enhanced scalability, making it well-suited for location-aware massive
connectivity driven by emerging wireless communication technologies.

FeMLoc 是一个联邦元学习框架，专注于室内定位领域，通过在众多边缘设备的不同定位数据集上进行协同元训练，以及在新环境中的快速自适应，实现了较传统神经网络方法更准确、更快速的室内定位，同时大大减少了指纹数据收集和校准的工作量，具备适应新兴无线通信技术推动的大规模连接的可扩展性，还具备隐私保护的元学习特征。