Federated optimal transport domain adaptation using Internet of Things sensor data for activity recognition

Federated learning has recently gained interest in optimizing smart buildings by addressing privacy issues. Activity recognition (AR) is important in improving smart building applications using Internet of Things (IoT) sensor data, allowing HVAC systems optimization, energy efficiency, security, and occupant comfort. However, the development of AR applications is often limited due to labeled data scarcity. IoT data is difficult to collect due to privacy, cost, and time issues. Common unsupervised domain adaptation (UDA) methods address these issues by sharing information from labeled sources to unlabeled target domains. Most of these approaches require direct access to labeled source data, raising privacy issues. This research proposes novel approaches that integrate federated learning (FL) with optimal transport domain adaptation (OTDA) to perform activity recognition while respecting privacy concerns. Optimal transport (OT)-based approaches have gained much interest thanks to their effective probability distribution alignment and improved generalization between source and target data features. Also, our novel methods use federated learning to allow the training of several models from multiple smart building sources without sharing the local data of each source domain. FL creates a robust global model that combines the performance of several local models to perform AR. This research paper introduces a novel integration of federated learning and optimal transport domain adaptation to achieve privacy-preserving and activity recognition across heterogeneous domains, which represents a new contribution to smart building research. We have developed two federated learning methods (federated averaging (FedAvg) and federated proximal (FedProx)) with optimal transport domain adaptation (FL-OTDA) called FedAvg-OTDA and FedProx-OTDA. Experimental results of global models on unseen AR datasets, with scores up to 71%, prove that the developed FL-OTDA methods are promising for the current smart building task. We provide the code in the following repository: https://github.com/JawDri/Federated-Learning-Optimal-Transport-Domain-Adaptation-for-Activity-Recognition.git .