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.

该研究通过使用机器学习技术来进行视频中的暴力检测，并在联邦学习环境下进行了适应性调整。通过实验使用了来自基准视频数据集中提取的时空特征，比较了不同方法，并提出了一种名为 “Diff-Gated” 的修改版本的 “Flow-Gated” 架构。此外，还探索了各种机器学习技术，包括超收敛和迁移学习，并开发了一种将中心化数据集适应于联邦学习环境的方法。通过在联邦学习环境中训练最佳的暴力检测模型，研究取得了与最先进模型相比更好的准确性结果。