MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows

TL;DR

MetisFL introduces a highly scalable federation controller for federated learning, significantly accelerating large-scale workflows and outperforming existing systems in execution time.

Contribution

The paper presents MetisFL, a novel federated learning system with an optimized, parallelized controller that enhances scalability and efficiency for large-scale FL workflows.

Findings

Achieves a 10-fold reduction in execution time compared to state-of-the-art systems.

Effectively handles increasing model sizes and federation sites.

Demonstrates significant performance improvements in diverse FL workflows.

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 controller is the first-class citizen. MetisFL re-engineers all the operations conducted by the federation controller to accelerate the training of large-scale FL workflows. By quantitatively comparing MetisFL against other state-of-the-art FL systems, we empirically demonstrate that MetisFL leads to a 10-fold wall-clock time execution boost across a wide range of challenging FL workflows with increasing model sizes and federation sites.