首页|Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos:A Study of Neural Network Architectures
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos:A Study of Neural Network Architectures
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万方数据
维普
This paper presents an original investigation into the domain of violence detection in videos,introducing an innovative approach tailored to the unique challenges of a federated learning environment.The study encompasses a com-prehensive 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 Gat-ed"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 federat-ed 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 po-tential of our contributions.This work represents a significant step forward in the application of machine learning tech-niques to critical societal challenges.
万方数据
维普
