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...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10614580/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850114263903895552 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Zahraa Khduair Taha |
| collection | DOAJ |
| description | 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 is suggested, a system that integrates federated local preprocessing, adaptive thresholding based on uncertainty symmetry, and a density- sensitive synthetic minority over-sampling approach. Each client preprocesses data locally and employs DS2MOTE for class balancing. On the server side, adaptive thresholding based on uncertainty symmetry is utilized to identify the optimal client for training the global mode. Evaluation on two distinct datasets—Human Activity Recognition with Smartphones and Human Activity Recognition (OpenPose) —reveals that our model outperforms FedAvg, FedSgd, FedSmote, and FedNova, achieving accuracies of 90.57% and 96.58%, respectively. In addition, FLP-DS2MOTE-USA minimizes update size and network overhead on the Human Activity Recognition with Smartphones, while achieving improvements on the OpenPose dataset. Overall, the proposed method not only addresses issues of imbalanced data but also reduces computational complexity via streamlined local preprocessing, and server-side mechanisms ensure client privacy. It outperforms traditional federated learning techniques in both accuracy and efficiency. |
| format | Article |
| id | doaj-art-9e91666ff9044a7a9335d126f99e0044 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9e91666ff9044a7a9335d126f99e00442025-08-20T02:36:57ZengIEEEIEEE Access2169-35362024-01-011218627718629510.1109/ACCESS.2024.343591010614580Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced DataZahraa Khduair Taha0https://orcid.org/0000-0003-4889-9679Johnny Koh Siaw Paw1Yaw Chong Tak2https://orcid.org/0000-0002-5044-6110Tiong Sieh Kiong3https://orcid.org/0000-0002-4447-262XKumaran Kadirgama4Foo Benedict5Tan Jian Ding6Kharudin Ali7Azher M. Abed8College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Kajang, MalaysiaInstitute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Kajang, MalaysiaInstitute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Kajang, MalaysiaInstitute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Kajang, MalaysiaFaculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, MalaysiaEnhance Track Sdn. Bhd., Puchong, Selangor, MalaysiaSchool of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Sepang, Selangor, MalaysiaFaculty of Engineering Technology (Electrical and Automation), University College TATI, Kemaman, Terengganu, MalaysiaCollege of Engineering and Technologies, Al-Mustaqbal University, Babylon, IraqFederated 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 is suggested, a system that integrates federated local preprocessing, adaptive thresholding based on uncertainty symmetry, and a density- sensitive synthetic minority over-sampling approach. Each client preprocesses data locally and employs DS2MOTE for class balancing. On the server side, adaptive thresholding based on uncertainty symmetry is utilized to identify the optimal client for training the global mode. Evaluation on two distinct datasets—Human Activity Recognition with Smartphones and Human Activity Recognition (OpenPose) —reveals that our model outperforms FedAvg, FedSgd, FedSmote, and FedNova, achieving accuracies of 90.57% and 96.58%, respectively. In addition, FLP-DS2MOTE-USA minimizes update size and network overhead on the Human Activity Recognition with Smartphones, while achieving improvements on the OpenPose dataset. Overall, the proposed method not only addresses issues of imbalanced data but also reduces computational complexity via streamlined local preprocessing, and server-side mechanisms ensure client privacy. It outperforms traditional federated learning techniques in both accuracy and efficiency.https://ieeexplore.ieee.org/document/10614580/Federated learninglocal preprocessingimbalance datauncertainty symmetry |
| spellingShingle | 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 Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data IEEE Access Federated learning local preprocessing imbalance data uncertainty symmetry |
| title | Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data |
| title_full | Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data |
| title_fullStr | Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data |
| title_full_unstemmed | Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data |
| title_short | Advances in Federated Learning: Combining Local Preprocessing With Adaptive Uncertainty Symmetry to Reduce Irrelevant Features and Address Imbalanced Data |
| title_sort | advances in federated learning combining local preprocessing with adaptive uncertainty symmetry to reduce irrelevant features and address imbalanced data |
| topic | Federated learning local preprocessing imbalance data uncertainty symmetry |
| url | https://ieeexplore.ieee.org/document/10614580/ |
| work_keys_str_mv | AT zahraakhduairtaha advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata AT johnnykohsiawpaw advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata AT yawchongtak advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata AT tiongsiehkiong advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata AT kumarankadirgama advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata AT foobenedict advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata AT tanjianding advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata AT kharudinali advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata AT azhermabed advancesinfederatedlearningcombininglocalpreprocessingwithadaptiveuncertaintysymmetrytoreduceirrelevantfeaturesandaddressimbalanceddata |