Optimizing Privacy and Processing: Navigating Federated Learning in the Era of Edge Computing

Haocheng Liu

2024

Abstract

Edge Computing (EC)is an emerging architecture that brings Cloud Computing (CC) services nearer to data sources. When integrated with Deep Learning (DL), EC becomes a highly promising technology and finds extensive application across various fields. This paper investigates the dynamic intersection of Federated Learning (FL) and Edge Computing, two forefront technological paradigms set to redefine data handling and machine learning at the network's edge. With the exponential rise in data from edge devices, FL presents a paradigm shift prioritizing user privacy, where data remains localized while contributing to a collective learning model. This work delves into the inherent challenges—data heterogeneity, varying computational capacities, and intermittent connectivity. It evaluates current methodologies, highlights advancements in algorithmic strategies to ensure robust and efficient distributed learning, and discusses potential applications. Future directions are examined, suggesting novel approaches for adaptive, privacy-preserving, and scalable machine learning solutions, thus catering to the nuanced demands of real-time, decentralized data processing.

Paper Citation

in Harvard Style

Liu H. (2024). Optimizing Privacy and Processing: Navigating Federated Learning in the Era of Edge Computing. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 704-707. DOI: 10.5220/0012969100004508

in Bibtex Style

@conference{emiti24,
author={Haocheng Liu},
title={Optimizing Privacy and Processing: Navigating Federated Learning in the Era of Edge Computing},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={704-707},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012969100004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Optimizing Privacy and Processing: Navigating Federated Learning in the Era of Edge Computing
SN - 978-989-758-713-9
AU - Liu H.
PY - 2024
SP - 704
EP - 707
DO - 10.5220/0012969100004508
PB - SciTePress