持有资料: Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data

Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data

Federated learning is increasingly being considered for sensor-driven human activity recognition, offering advantages in terms of privacy and scalability compared to centralized methods. However, challenges such as feature selection and client imbalanced data persist. In this study, FLP-DS2MOTE-USA...

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Main Authors:	Taha Z.K., Paw J.K.S., Tak Y.C., Kiong T.S., Kadirgama K., Benedict F., Ding T.J., Ali K., Abed A.M.
其他作者:	57202301078
格式:	Article
出版:	Institute of Electrical and Electronics Engineers Inc. 2025
主题:	Data privacy Data reduction Feature extraction Accuracy Dimensionality reduction Features extraction Federated learning Human activity recognition Imbalance datum Local preprocessing Uncertainty Uncertainty symmetry Smartphones
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Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data

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