Hierarchical Federated Learning: An Analytical Perspective with Stochastic Petri Nets

• Israel Araújo UFPI
• Luís Guilherme Silva UFPI
• Francinaldo Barbosa UFPI
• Iure Fé UFPI
• Geraldo P. Rocha Filho UESB
• Francisco Airton Silva UFPI

DOI: https://doi.org/10.5753/sbrc.2026.19936

Abstract

Evaluating Hierarchical Federated Learning (HFL) in dense environments is difficult due to scalability limitations and network and resource variability. Although hierarchical architectures employ intermediate servers for partial aggregations, analyzing their behavior through simulation or experimentation is still costly to explore multiple configurations. This paper proposes a Stochastic Petri Net (SPN) model to represent HFL with a central server and intermediate servers. The model estimates metrics for round completion rate, average round time, and discard probability. The results show that increasing the number of intermediate servers improves throughput, reduces average round time, and expands operational capacity under high loads. In one evaluated scenario, expanding from two to four intermediate servers reduced the average round time by more than 60% and increased the round completion rate by up to 160%.

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