Balancing Accuracy and Training Time in Federated Learning for Violence
  Detection in Surveillance Videos: A Study of Neural Network Architectures
Pajon Quentin Serre Swan Wissocq Hugo Rabaud Léo Haidar Siba Yaacoub Antoun
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.