Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos
This provides a privacy-preserving solution for improving surveillance systems in urban environments, though it appears incremental as it applies an existing PFL method to a specific domain.
The paper tackled violence detection in surveillance videos by using Personalized Federated Learning to adapt models to heterogeneous data, achieving up to 99.3% accuracy.
The challenge of detecting violent incidents in urban surveillance systems is compounded by the voluminous and diverse nature of video data. This paper presents a targeted approach using Personalized Federated Learning (PFL) to address these issues, specifically employing the Federated Learning with Personalization Layers method within the Flower framework. Our methodology adapts learning models to the unique data characteristics of each surveillance node, effectively managing the heterogeneous and non-IID nature of surveillance video data. Through rigorous experiments conducted on balanced and imbalanced datasets, our PFL models demonstrated enhanced accuracy and efficiency, achieving up to 99.3% accuracy. This study underscores the potential of PFL to significantly improve the scalability and effectiveness of surveillance systems, offering a robust, privacy-preserving solution for violence detection in complex urban environments.