Hyperparameter Impact on Computational Efficiency in Federated Edge Learning

The heterogeneity induced by the federated edge learning execution environment poses many performance challenges. Indeed, a balance between efficient resource usage and inference accuracy must be found. Our work therefore aims at characterizing the hyperparameter influence by creating a variety of simulated execution circumstances. We designed an experimentation platform to simulate the execution of a typical image recognition training workload to highlight tweaking opportunities. We particularly focus on participant selection as an important performance lever. Thus, our benchmarks vary the number of clients participating in the federated edge learning process within i.i.d. and non-i.i.d. environments, while illustrating real-world configurations based on heterogeneous edge systems. We identify computational efficiency facets in federated edge learning and propose a taxonomic methodology to approach the study. We demonstrate the impact of the number of clients selected to participate in the global model update of federated edge learning on the overall system computational efficiency in challenging environments. Thus, we propose an optimization formula to meet computational efficiency and accurate models in challenging federated edge learning environments.