# FLEET: A Federated Learning Emulation and Evaluation Testbed for Holistic Research

## TL;DR

FLEET is a comprehensive testbed that emulates realistic network conditions to evaluate federated learning algorithms, bridging the gap between theoretical design and practical deployment.

## Contribution

The paper introduces FLEET, a scalable, configurable platform integrating a versatile learning component with a high-fidelity network emulator for holistic FL research.

## Key findings

- Supports diverse ML frameworks and real-world network topologies
- Enables systematic study of network effects on FL convergence
- Provides holistic metrics linking network conditions to algorithm performance

## Abstract

Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challenge, we introduce the Federated Learning Emulation and Evaluation Testbed (FLEET). This comprehensive platform provides a scalable and configurable environment by integrating a versatile, framework-agnostic learning component with a high-fidelity network emulator. FLEET supports diverse machine learning frameworks, customizable real-world network topologies, and dynamic background traffic generation. The testbed collects holistic metrics that correlate algorithmic outcomes with detailed network statistics. By unifying the entire experiment configuration, FLEET enables researchers to systematically investigate how network constraints, such as limited bandwidth, high latency, and packet loss, affect the convergence and efficiency of FL algorithms. This work provides the research community with a robust tool to bridge the gap between algorithmic theory and real-world network conditions, promoting the holistic and reproducible evaluation of federated learning systems.