Exploration and Analysis of FedAvg, FedProx, FedMA, MOON, and FedProc Algorithms in Federated Learning

Jinlin Li

2024

Abstract

In the data-driven modern era, machine learning is crucial, yet it poses challenges to data privacy and security. To address this issue, federated learning, as an emerging paradigm of distributed machine learning, enables multiple participants to collaboratively train a shared model without the need to share raw data, effectively safeguarding individual privacy. This study delves into federated learning, analyzing key algorithms such as Federated Averaging algorithm (FedAvg), Federated Proximal Algorithm (FedProx), Federated Matched Averaging (FedMA), and Prototypical Contrastive Federated Learning (FedProc). These algorithms offer unique solutions to core challenges within federated learning, such as dealing with non-independent and identically distributed (non-IID) data, optimizing communication efficiency, and enhancing model performance. This paper provides a comparative analysis of the performance of these algorithms, discussing their advantages and limitations in addressing specific problems and challenges. A comprehensive understanding of modern federated learning algorithms suggests that selecting an appropriate federated learning algorithm requires consideration of specific application needs, data characteristics, and model complexity.

Paper Citation

in Harvard Style

Li J. (2024). Exploration and Analysis of FedAvg, FedProx, FedMA, MOON, and FedProc Algorithms in Federated Learning. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 172-176. DOI: 10.5220/0012836400004547

in Bibtex Style

@conference{icdse24,
author={Jinlin Li},
title={Exploration and Analysis of FedAvg, FedProx, FedMA, MOON, and FedProc Algorithms in Federated Learning},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={172-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012836400004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Exploration and Analysis of FedAvg, FedProx, FedMA, MOON, and FedProc Algorithms in Federated Learning
SN - 978-989-758-690-3
AU - Li J.
PY - 2024
SP - 172
EP - 176
DO - 10.5220/0012836400004547
PB - SciTePress