FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks

arXiv:2405.11079v1 Announce Type: cross
Abstract: 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 …

abstract arxiv calibration collaboration collection connected devices cs.lg cs.ni data data collection devices dynamic eess.sp environment growth however internet internet of things iot iot networks localization meta meta-learning networks scalability solutions struggle tasks type wireless