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: Zahraa Khduair Taha, Johnny Koh Siaw Paw, Yaw Chong Tak, Tiong Sieh Kiong, Kumaran Kadirgama, Foo Benedict, Tan Jian Ding, Kharudin Ali, Azher M. Abed
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Federated learning
local preprocessing
imbalance data
uncertainty symmetry
Online Access:https://ieeexplore.ieee.org/document/10614580/
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https://ieeexplore.ieee.org/document/10614580/

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