  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.
