  This paper presents an investigation into machine learning techniques for
violence detection in videos and their adaptation to a federated learning
context. The study includes experiments with spatio-temporal features extracted
from benchmark video datasets, comparison of different methods, and proposal of
a modified version of the "Flow-Gated" architecture called "Diff-Gated."
Additionally, various machine learning techniques, including super-convergence
and transfer learning, are explored, and a method for adapting centralized
datasets to a federated learning context is developed. The research achieves
better accuracy results compared to state-of-the-art models by training the
best violence detection model in a federated learning context.
