FDSTCN-EEG achieved 96.96% accuracy in EEG-based seizure detection, reducing parameters by 40.4% and speeding up training by 38.5% compared to synchronous learning.
Does the FDSTCN-EEG federated learning framework improve computational efficiency and training speed while maintaining high accuracy for decentralized EEG seizure detection?
FDSTCN-EEG provides a highly accurate, privacy-preserving, and computationally efficient federated learning framework for real-world EEG seizure monitoring.
Absolute Event Rate: 0% vs 0%
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 real-world seizure monitoring systems. Such work introduces a practical privacy-preserving medical AI paradigm, which balances model efficiency with training scalability and clinical quality accuracy.
Lim et al. (Tue,) reported a other. FDSTCN-EEG achieved 96.96% accuracy in EEG-based seizure detection, reducing parameters by 40.4% and speeding up training by 38.5% compared to synchronous learning.