LiCAFeL-STC: A Lightweight Cluster-Based Federated Learning Framework for Sensor-Based Human Activity Recognition Using Unlabeled Data in Heterogeneous Wearable Devices
Author:
Affiliation:
1. Hankuk University of Foreign Studies,Department of Industrial and Management Engineering,Yongin,South Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10633276/10633246/10633549.pdf?arnumber=10633549
Reference18 articles.
1. A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions
2. A Comprehensive Survey on Federated Learning: Concept and Applications
3. Human Activity Recognition with Wearables using Federated Learning;Jovanovski,2023
4. SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training;Diao;Advances in Neural Information Processing Systems,2022
5. Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR
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