Empirical Analysis of Federated Learning Algorithms: A Federated Research Infrastructure Use Case

Harshit Gupta, Abhishek Verma, O. Vyas, Marco Garofalo, Marco Garofalo, Giuseppe Tricomi, Giuseppe Tricomi, Francesco Longo, Francesco Longo, Giovanni Merlino, Giovanni Merlino, Antonio Puliafito, Antonio Puliafito

2023

Abstract

Research Infrastructures provide resources and services for communities of researchers at large to conduct their experiments and foster innovation. Moreover, these can be used beyond research, e.g., for education or public service. The SLICES consortium is chartered to provide a fully programmable, distributed, virtualized, remotely accessible, European-wide, federated research infrastructure, providing advanced computing, storage, and networking capabilities, including interconnection by dedicated high-speed links. It will support large-scale, experimental research across various scientific domains. Data processing, in general, and especially Machine Learning, are of great interest to the potential audience of SLICES. According to these premises, this work aims to exploit such a peculiar Research Infrastructure and its Cloud-oriented development and deployment facilities to investigate Federated Learning (FL) approaches; in particular, here we evaluate the performance of two FL aggregation algorithms, i.e., FedAvg and FedProx, in settings, characterized by system heterogeneity, and statistical heterogeneity, that represent plausible, and possibly common, scenarios in forthcoming facilities, such as those mentioned above, community-oriented, shared Research Infrastructures. We have observed that the FedProx algorithm outperforms the FedAvg algorithm in such settings.

Paper Citation

in Harvard Style

Gupta H., Verma A., Vyas O., Garofalo M., Tricomi G., Longo F., Merlino G. and Puliafito A. (2023). Empirical Analysis of Federated Learning Algorithms: A Federated Research Infrastructure Use Case. In Proceedings of the 13th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-650-7, SciTePress, pages 324-331. DOI: 10.5220/0012037600003488

in Bibtex Style

@conference{closer23,
author={Harshit Gupta and Abhishek Verma and O. Vyas and Marco Garofalo and Giuseppe Tricomi and Francesco Longo and Giovanni Merlino and Antonio Puliafito},
title={Empirical Analysis of Federated Learning Algorithms: A Federated Research Infrastructure Use Case},
booktitle={Proceedings of the 13th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2023},
pages={324-331},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012037600003488},
isbn={978-989-758-650-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Empirical Analysis of Federated Learning Algorithms: A Federated Research Infrastructure Use Case
SN - 978-989-758-650-7
AU - Gupta H.
AU - Verma A.
AU - Vyas O.
AU - Garofalo M.
AU - Tricomi G.
AU - Longo F.
AU - Merlino G.
AU - Puliafito A.
PY - 2023
SP - 324
EP - 331
DO - 10.5220/0012037600003488
PB - SciTePress