On the Reproducibility Challenges of Federated Learning: Investigating the Gap Between Simulation, Emulation and Real-World Deployments
Cédric Prigent, Kate Keahey, Alexandru Costan, Loïc Cudennec, Gabriel Antoniu · 2025
Federated Learning (FL) is an emerging paradigm for decentralized training of Machine Learning models. It has been the subject of a large corpus of research due to its innovative approach to handling sensitive data. A common practice in the FL literature is to run simulations on a single compute node to assess the performance of FL algorithms. While simulation enables fast prototyping and validation of algorithmic concepts, it may face limitations in reproducing the real system's performance in heterogeneous environments such as the Computing Continuum, and particularly on resource-constrained Edge devices. Conversely, emulation on distributed testbeds offers more effective means to accurately reproduce the performance of real-world devices. However, to the best of our knowledge, no prior research has investigated the differences between simulation and emulation in FL experiments. In this paper, we study the complementarity of these approaches and discuss their respective challenges, as a first step towards reproducibility of FL experiments. We illustrate our study with a real-life application used as a baseline: an outdoor air quality forecasting framework with real-world sensors. Our results show that simulation can be used to accurately reproduce model performance metrics, while emulation can effectively reproduce the system performance of real-world experiments. Finally, we present a set of lessons learned on the challenges of FL reproducibility and the selection of experimental infrastructures for FL experiments and applications.