Citation

Lim, Zheng You and Pang, Ying Han and Ooi, Shih Yin and Khoh, Wee How and Chew, Yee Jian (2026) FDSTCN-EEG: Federated Depthwise Separable Temporal Convolutional Networks for Decentralized EEG Seizure Detection. AI, 7 (3). p. 101. ISSN 2673-2688

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Abstract

In this paper, we propose FDSTCN-EEG, which is a customized federated learning framework for EEG-based seizure detection that leverages deep depthwise separable temporal convolutions and asynchronous model aggregation. The network design tackles major problems in distributed healthcare AI by jointly boosting computational efficiency, training rate, and classification performance. In this paper, we propose FDSTCN-EEG, a novel federated learning framework specifically designed for EEG-based seizure detection. Our key contributions are threefold: first, high architectural efficiency with depthwise separable temporal convolutions, reducing parameters by 40.4% (9.8M to 5.8M) while maintaining accuracy of 96.96%; second, speeding up training by a factor of 38.5% compared with synchronous learning via an asynchronous aggregation protocol; finally, a privacy-preserving decentralized learning model without sharing raw EEG data and with the capability of coping with the heterogeneous clinical technology infrastructure. Extensive experiments show superior performance (accuracy: 96.96%, F1-score: 97.02%) and practical viability for realworld seizure monitoring systems. Such work introduces a practical privacy-preserving medical AI paradigm, which balances model efficiency with training scalability and clinical quality accuracy.

Item Type:	Article
Uncontrolled Keywords:	Temporal convolutional networks
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Apr 2026 02:46
Last Modified:	03 Apr 2026 02:46
URII:	http://shdl.mmu.edu.my/id/eprint/15687

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