Research Advanced in Personalized Federated Learning

Zizhuo Wang

2024

Abstract

Federated learning eliminates the need for local device data sharing by enabling diverse users to team up to build shared global models, and has gradually become a research hotspot in machine learning communities in recent years. Despite being widely used in many application fields, the convergence of disparate data types and the absence of tailored solutions continue to pose challenges for federated learning models. In this context, pFL(personalized federated learning) has rapidly developed, aimed at reducing heterogeneity and creating personalized models for each device through personalized processing at the device, data, and model levels. The latest research progress in personalized federated learning is systematically reviewed in this article. Focusing on the aspects of global model personalization and learning personalized models, this article first introduces representative personalized federated learning algorithms, including their design ideas and key steps. This article also discusses the challenges in the field of personalized federated learning and looks forward to its future development direction, which aims to bring more new insight to this field.

Paper Citation

in Harvard Style

Wang Z. (2024). Research Advanced in Personalized Federated Learning. 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 536-540. DOI: 10.5220/0012958400004508

in Bibtex Style

@conference{emiti24,
author={Zizhuo Wang},
title={Research Advanced in Personalized Federated Learning},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={536-540},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012958400004508},
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 - Research Advanced in Personalized Federated Learning
SN - 978-989-758-713-9
AU - Wang Z.
PY - 2024
SP - 536
EP - 540
DO - 10.5220/0012958400004508
PB - SciTePress