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.
FLWB: a Workbench Platform for Performance Evaluation of Federated Learning Algorithms / Casalicchio, E.; Esposito, S.; Al-Saedi, A. A.. - (2023), pp. 401-405. (Intervento presentato al convegno 2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023 tenutosi a ita) [10.1109/TechDefense59795.2023.10380832].
FLWB: a Workbench Platform for Performance Evaluation of Federated Learning Algorithms
Casalicchio E.
Primo
Writing – Original Draft Preparation
;
2023
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
