Scalable and Portable Federated Learning Simulation Engine
3Presenter: Borja Arroyo Galende
Investigador H2020 en GATV UPM
Abstract: Federated learning (FL) is one of the most promising approaches to ensure privacy in the application of data-driven techniques to sensitive information. However, the implementation of such approaches in a production environment is still an important challenge. In this paper, we present a scalable, portable, hardware-independent, model-agnostic FL Simulation Engine (FLSE) with the aim of easing the job of researchers who want to train FL models to be deployed in production environments.
The FLSE offers a tool that can be used both standalone or embedded within a larger architecture, it can be deployed seamlessly and allows concurrent, scalable, and highly available V&V assessment support for FL models. The tool allows researchers to understand the behaviour, in terms of metric performance, of their proposed models in production scenarios, allowing a boost in trustworthiness towards ethical AI.
eSAAM 2023: https://events.eclipse.org/2023/esaam2023/
Sponsored by:
- https://he-codeco.eu/
- https://eucloudedgeiot.eu/
- https://meta-os.eu/
- https://nephele-project.eu/
- https://eclipse.org/