BouquetFL: Emulating diverse participant hardware in Federated Learning

TL;DR

BouquetFL is a framework that simulates diverse hardware configurations in federated learning experiments on a single machine, enabling realistic studies of system heterogeneity without multiple physical devices.

Contribution

It introduces BouquetFL, a novel tool for emulating heterogeneous client hardware in federated learning, bridging the gap between simulation and real-world deployment conditions.

Findings

Enables controlled experiments with hardware diversity

Supports a wide range of device profiles

Facilitates research on system heterogeneity in FL

Abstract

In Federated Learning (FL), multiple parties collaboratively train a shared Machine Learning model to encapsulate all private knowledge without exchange of information. While it has seen application in several industrial projects, most FL research considers simulations on a central machine, without considering potential hardware heterogeneity between the involved parties. In this paper, we present BouquetFL, a framework designed to address this methodological gap by simulating heterogeneous client hardware on a single physical machine. By programmatically emulating diverse hardware configurations through resource restriction, BouquetFL enables controlled FL experimentation under realistic hardware diversity. Our tool provides an accessible way to study system heterogeneity in FL without requiring multiple physical devices, thereby bringing experimental practice closer to practical deployment conditions. The target audience are FL researchers studying highly heterogeneous federations. We include a wide range of profiles derived from commonly available consumer and small-lab devices, as well as a custom hardware sampler built on real-world hardware popularity, allowing users to configure the federation according to their preference.