Advancements in Personalized Federated Learning for Epileptic Seizure Detection

Rachitha E., M S Bhargavi

2024

Abstract

Personalized federated learning for Epileptic seizure detection represents a promising avenue for improving the accuracy and efficiency of seizure detection systems while safeguarding individual privacy. Epilepsy is a neurological disorder characterized by recurrent seizures, and timely detection of these events is critical for effective management and intervention. Traditional centralized approaches to seizure detection face challenges related to data privacy, scalability, and diversity of data sources. Federated learning (FL) offers a decentralized paradigm where models are trained cooperatively across various clients or data silos without centralizing sensitive data. This study discusses the state-of-the-art in personalized federated learning for epileptic seizure detection. The study focuses on the fundamentals of federated learning and its applicability to healthcare settings, especially with regard to epilepsy management. Recent advancements in personalized seizure detection algorithms tailored to federated learning settings, machine / deep learning models, client /data distribution and performance are reviewed. Furthermore, challenges and opportunities in deploying federated learning systems for epileptic seizure detection are examined. Finally, insights into the current landscape of personalized federated learning for epileptic seizure detection are discussed with experimental analysis inspiring further research.

Paper Citation

in Harvard Style

E. R. and Bhargavi M. (2024). Advancements in Personalized Federated Learning for Epileptic Seizure Detection. In Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com; ISBN 978-989-758-739-9, SciTePress, pages 222-231. DOI: 10.5220/0013311500004646

in Bibtex Style

@conference{ic3com24,
author={Rachitha E. and M S Bhargavi},
title={Advancements in Personalized Federated Learning for Epileptic Seizure Detection},
booktitle={Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com},
year={2024},
pages={222-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013311500004646},
isbn={978-989-758-739-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com
TI - Advancements in Personalized Federated Learning for Epileptic Seizure Detection
SN - 978-989-758-739-9
AU - E. R.
AU - Bhargavi M.
PY - 2024
SP - 222
EP - 231
DO - 10.5220/0013311500004646
PB - SciTePress