MiniNetFED: A Tool for Emulation and Analysis of Federated Learning with Heterogeneous Devices

• Johann Jakob Schmitz Bastos UFES
• Eduardo Montagner de Moraes Sarmento UFES
• Rodolfo da Silva Villaça UFES
• Vinícius F. S. Mota UFES

DOI: https://doi.org/10.5753/sbrc_estendido.2024.3226

Abstract

This paper presents the MiniNetFed, an emulation tool for analyzing federated learning algorithms. The MiniNetFed enables the emulation of an environment with heterogeneous devices, differing in processing power, memory, and network capability, for conducting federated learning experiments. The tool also allows users to define: i) data partition formats among devices; ii) client selection policies; iii) model aggregation functions; iv) pre-trained models for major datasets used in the literature; and v) graphical visualizations of key performance metrics. Finally, besides its educational potential, the MiniNetFed is designed so that new algorithms and models can be easily extended, allowing researchers to implement and evaluate their proposals.

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