Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383720765> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W4383720765 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." @default.
- W4383720765 created "2023-07-11" @default.
- W4383720765 creator A5008614196 @default.
- W4383720765 creator A5013844176 @default.
- W4383720765 creator A5058233812 @default.
- W4383720765 creator A5058253407 @default.
- W4383720765 creator A5059152392 @default.
- W4383720765 date "2023-07-10" @default.
- W4383720765 modified "2023-09-26" @default.
- W4383720765 title "MoFLeuR: Motion-based Federated Learning Gesture Recognition" @default.
- W4383720765 doi "https://doi.org/10.22541/au.168898342.22939562/v1" @default.
- W4383720765 hasPublicationYear "2023" @default.
- W4383720765 type Work @default.
- W4383720765 citedByCount "0" @default.
- W4383720765 crossrefType "posted-content" @default.
- W4383720765 hasAuthorship W4383720765A5008614196 @default.
- W4383720765 hasAuthorship W4383720765A5013844176 @default.
- W4383720765 hasAuthorship W4383720765A5058233812 @default.
- W4383720765 hasAuthorship W4383720765A5058253407 @default.
- W4383720765 hasAuthorship W4383720765A5059152392 @default.
- W4383720765 hasBestOaLocation W43837207651 @default.
- W4383720765 hasConcept C104114177 @default.
- W4383720765 hasConcept C110875604 @default.
- W4383720765 hasConcept C111919701 @default.
- W4383720765 hasConcept C11413529 @default.
- W4383720765 hasConcept C119857082 @default.
- W4383720765 hasConcept C121687571 @default.
- W4383720765 hasConcept C124101348 @default.
- W4383720765 hasConcept C136764020 @default.
- W4383720765 hasConcept C154945302 @default.
- W4383720765 hasConcept C159437735 @default.
- W4383720765 hasConcept C162307627 @default.
- W4383720765 hasConcept C162324750 @default.
- W4383720765 hasConcept C207347870 @default.
- W4383720765 hasConcept C2777303404 @default.
- W4383720765 hasConcept C2778456923 @default.
- W4383720765 hasConcept C2992525071 @default.
- W4383720765 hasConcept C41008148 @default.
- W4383720765 hasConcept C45374587 @default.
- W4383720765 hasConcept C50522688 @default.
- W4383720765 hasConcept C79061980 @default.
- W4383720765 hasConcept C89805583 @default.
- W4383720765 hasConcept C98045186 @default.
- W4383720765 hasConceptScore W4383720765C104114177 @default.
- W4383720765 hasConceptScore W4383720765C110875604 @default.
- W4383720765 hasConceptScore W4383720765C111919701 @default.
- W4383720765 hasConceptScore W4383720765C11413529 @default.
- W4383720765 hasConceptScore W4383720765C119857082 @default.
- W4383720765 hasConceptScore W4383720765C121687571 @default.
- W4383720765 hasConceptScore W4383720765C124101348 @default.
- W4383720765 hasConceptScore W4383720765C136764020 @default.
- W4383720765 hasConceptScore W4383720765C154945302 @default.
- W4383720765 hasConceptScore W4383720765C159437735 @default.
- W4383720765 hasConceptScore W4383720765C162307627 @default.
- W4383720765 hasConceptScore W4383720765C162324750 @default.
- W4383720765 hasConceptScore W4383720765C207347870 @default.
- W4383720765 hasConceptScore W4383720765C2777303404 @default.
- W4383720765 hasConceptScore W4383720765C2778456923 @default.
- W4383720765 hasConceptScore W4383720765C2992525071 @default.
- W4383720765 hasConceptScore W4383720765C41008148 @default.
- W4383720765 hasConceptScore W4383720765C45374587 @default.
- W4383720765 hasConceptScore W4383720765C50522688 @default.
- W4383720765 hasConceptScore W4383720765C79061980 @default.
- W4383720765 hasConceptScore W4383720765C89805583 @default.
- W4383720765 hasConceptScore W4383720765C98045186 @default.
- W4383720765 hasLocation W43837207651 @default.
- W4383720765 hasOpenAccess W4383720765 @default.
- W4383720765 hasPrimaryLocation W43837207651 @default.
- W4383720765 hasRelatedWork W2044054641 @default.
- W4383720765 hasRelatedWork W2044642157 @default.
- W4383720765 hasRelatedWork W2093841401 @default.
- W4383720765 hasRelatedWork W2156762151 @default.
- W4383720765 hasRelatedWork W2184652164 @default.
- W4383720765 hasRelatedWork W2368800436 @default.
- W4383720765 hasRelatedWork W2896395763 @default.
- W4383720765 hasRelatedWork W2982047694 @default.
- W4383720765 hasRelatedWork W3045242333 @default.
- W4383720765 hasRelatedWork W3155873301 @default.
- W4383720765 isParatext "false" @default.
- W4383720765 isRetracted "false" @default.
- W4383720765 workType "article" @default.