Abstract
In recent years, Deep Neural Networks (DNNs) have been widely
used for Human Gesture Recognition (HGR) based on the information
obtained from inertial sensors, such as accelerometers and gyroscopes,
available on smart Internet of Things (IoT) devices. Most of the recent
works on HGR using motion data rely on gathering a dataset, that faces
two major challenges: a) the datasets are originally
stored on the smart devices at the end-users, and gathering them in one
place is not feasible due to communication limitations, and b)
clients are reluctant to share their private data with a central
server due to privacy concerns. In this paper, we address these issues
and propose a privacy-preserving framework based on Federated Learning
(FL) for HGR using motion data, called Motion-based Federated Learning
Gesture Recognition (MoFLeuR). Furthermore, we consider different types
of data heterogeneity which have destructive effects on the performance
of the global model. Accordingly, we propose a communication and
computation-efficient client selection method that chooses the clients
to mitigate the impact of data heterogeneity in the training process. In
the proposed framework, clients are not requested to share sensitive
information about their local datasets with the edge server in the FL
process. Simulation results show that the proposed MoFLeuR algorithm
improves the performance of the global model in the presence of
different degrees of data heterogeneity, and it outperforms the baseline
algorithms in terms of different metrics, namely accuracy, convergence
speed, and communication and computation efficiency.
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