Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures

This paper presents an original investigation into the domain of violence detection in videos, introducing innovative approaches tailored to the unique challenges of a federated learning environment. The study encompasses a comprehensive exploration of machine learning techniques, leveraging spatio-temporal features extracted from benchmark video datasets. In a notable departure from conventional methodologies, we introduce a novel architecture, the "Diff-Gated" network, designed to streamline preprocessing and training while simultaneously enhancing accuracy. Our exploration of advanced machine learning techniques, such as super-convergence and transfer learning, expands the horizons of federated learning, offering a broader range of practical applications. Moreover, our research introduces a method for seamlessly adapting centralized datasets to the federated learning context, bridging the gap between traditional machine learning and federated learning approaches. The outcome of this study is a remarkable advancement in the field of violence detection, with our federated learning model consistently outperforming state-of-the-art models, underscoring the transformative potential of our contributions. This work represents a significant step forward in the application of machine learning techniques to critical societal challenges.