FLWB: a Workbench Platform for Performance Evaluation of Federated Learning Algorithms
FLWB: a Workbench Platform for Performance Evaluation of Federated Learning Algorithms
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.
Learning projects, Learning systems, Computer Sciences, security, Deployment evaluations, Learning algorithms, Distributed devices, Performances evaluation, performance evaluation, Datavetenskap (datalogi), microservice, Research communities, Machine learning models, Federated Learning; microservice; performance evaluation; security, Custom simulators, Federated Learning
Learning projects, Learning systems, Computer Sciences, security, Deployment evaluations, Learning algorithms, Distributed devices, Performances evaluation, performance evaluation, Datavetenskap (datalogi), microservice, Research communities, Machine learning models, Federated Learning; microservice; performance evaluation; security, Custom simulators, Federated Learning
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