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, Zahraa Khduair, Siaw Paw, Johnny Koh, Tak, Yaw Chong, Kiong, Tiong Sieh, Kadirgama, Kumaran, Benedict, Foo, Ding, Tan Jian, Kharudin, Ali, Abed, Azher M.
Format: Article
Language:English
Published: IEEE 2024
Subjects:
QA75 Electronic computers. Computer science
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/44790/
http://umpir.ump.edu.my/id/eprint/44790/1/Advances%20in%20federated%20learning-Combining%20local%20preprocessing%20with%20adaptive.pdf

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http://umpir.ump.edu.my/id/eprint/44790/
http://umpir.ump.edu.my/id/eprint/44790/1/Advances%20in%20federated%20learning-Combining%20local%20preprocessing%20with%20adaptive.pdf

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