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Found 5 papers, 0 papers with code
FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks
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.
A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments
Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally.
Federated Learning based Hierarchical 3D Indoor Localization
The proliferation of connected devices in indoor environments opens the floor to a myriad of indoor applications with positioning services as key enablers.
Federated Distillation based Indoor Localization for IoT Networks
Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources.
Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning
The obtained results show an improvement of 48. 10% in the localization accuracy compared to the baseline DNN model.
