Holdings: 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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Bibliographic Details
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.
Other Authors:	57202301078
Format:	Article
Published:	Institute of Electrical and Electronics Engineers Inc. 2025
Subjects:	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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