Accelerating Federated Learning Within a Domain with Heterogeneous Data Centers

M. Vishnu, G. Anitha

2023

Abstract

In the current scenario accelerating with heterogeneous data centers tends to be required for federated learning in that case we have proposed a novel approach for accelerating the training process. The authors introduce a new communication-efficient algorithm called "Federated Momentum SGD," which reduces the amount of communication required between the data centers during the training process. They also present a technique for adjusting the learning rate to improve convergence speed. The proposed approach is evaluated on several benchmark datasets, and the results show significant improvements in training time and accuracy compared to existing methods. The authors conclude that their approach can effectively accelerate the domains that are within the federated learning data by this we could make the solution for large-scale machine learning tasks.

Paper Citation

in Harvard Style

Vishnu M. and Anitha G. (2023). Accelerating Federated Learning Within a Domain with Heterogeneous Data Centers. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 344-348. DOI: 10.5220/0012771300003739

in Bibtex Style

@conference{ai4iot23,
author={M. Vishnu and G. Anitha},
title={Accelerating Federated Learning Within a Domain with Heterogeneous Data Centers},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={344-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012771300003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Accelerating Federated Learning Within a Domain with Heterogeneous Data Centers
SN - 978-989-758-661-3
AU - Vishnu M.
AU - Anitha G.
PY - 2023
SP - 344
EP - 348
DO - 10.5220/0012771300003739
PB - SciTePress