FLWB: a Workbench Platform for Performance Evaluation of Federated Learning Algorithms
2023 (English)In: 2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 401-405Conference paper, Published paper (Refereed)
Abstract [en]
Federated learning is a technique that allows to collaboratively train a shared machine learning model across distributed devices, where the data are stored locally on devices. Most innovations the research community proposes in federated learning are tested through custom simulators. An analysis of the literature shows the lack of workbench platforms for the performance evaluation of FL projects. This paper aims to fill the gap by presenting FLWB, a general-purpose, configurable, and scalable workbench platform for easy deployment and performance evaluation of Federated Learning projects. Through experiments, we demonstrated the ease with which a FL system can be implemented and deployed with FLWB. © 2023 IEEE.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 401-405
Keywords [en]
Federated Learning, microservice, performance evaluation, security, Learning algorithms, Custom simulators, Deployment evaluations, Distributed devices, Learning projects, Machine learning models, Performances evaluation, Research communities, Learning systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-25969DOI: 10.1109/TechDefense59795.2023.10380832Scopus ID: 2-s2.0-85183927234ISBN: 9798350319392 (print)OAI: oai:DiVA.org:bth-25969DiVA, id: diva2:1838485
Conference
2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023, Rome, 20-22 Nov 2023
Part of project
HINTS - Human-Centered Intelligent Realities
Funder
Knowledge Foundation, 202200682024-02-162024-02-162024-02-26Bibliographically approved