Leveraging Federated Learning for Efficient Privacy-Enhancing Violent Activity Recognition from Videos

Automated recognition of violent activities from videos is vital for public safety, but often raises significant privacy concerns due to the sensitive nature of the footage. Moreover, resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices. With this motivation, this study aims to investigate an effective violent activity classifier while minimizing computational complexity, attaining competitive performance, and mitigating user data privacy concerns. We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition. We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction, incorporating federated learning to address data privacy concerns. Experimental findings demonstrate the model’s effectiveness with test accuracies from 96% to above 97% on multiple datasets by incorporating the FedProx aggregation strategy. These findings underscore the potential to develop secure, efficient, and reliable solutions for violent activity recognition in real-world scenarios.