Abstract

A Federated Learning (FL) system typically consists of two core processing entities: the federation controller and the learners. The controller is responsible for managing the execution of FL workflows across learners and the learners for training and evaluating federated models over their private datasets. While executing an FL workflow, the FL system has no control over the computational resources or data of the participating learners. Still, it is responsible for other operations, such as model aggregation, task dispatching, and scheduling. These computationally heavy operations generally need to be handled by the federation controller. Even though many FL systems have been recently proposed to facilitate the development of FL workflows, most of these systems overlook the scalability of the controller. To meet this need, we designed and developed a novel FL system called MetisFL, where the federation …

Date

December 8, 2023

Authors

Dimitris Stripelis, Chrysovalantis Anastasiou, Patrick Toral, Armaghan Asghar, José Luis Ambite

Book

Proceedings of the 4th International Workshop on Distributed Machine Learning

Pages

11-19