BouquetFL: Emulating diverse participant hardware in Federated Learning

2026-02-06

AI Summary

Existing federated learning research often lacks effective simulation of client hardware heterogeneity, leading to a disconnect between experimental results and real-world deployment. To address this gap, this work proposes BouquetFL, a configurable hardware heterogeneity emulation framework that, for the first time, enables programmable simulation of diverse consumer-grade and laboratory device performance on a single physical machine. By imposing resource constraints on CPU, memory, and bandwidth, BouquetFL accurately emulates heterogeneous hardware capabilities and integrates a custom hardware sampler informed by real-world device prevalence statistics. This approach efficiently reproduces realistic hardware distributions without requiring multiple physical devices, thereby providing a reliable and controllable experimental foundation for studying federated learning algorithms and systems under highly heterogeneous conditions.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Federated Learning
• hardware heterogeneity
• client diversity
• simulation
• system heterogeneity

Innovation

• Federated Learning
• Hardware Heterogeneity
• Resource Emulation
• Controlled Experimentation
• Simulation Framework