Open Access

ARTICLE

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

Moshiur Rahman Tonmoy¹, Md. Mithun Hossain¹, Mejdl Safran^2,*, Sultan Alfarhood², Dunren Che³, M. F. Mridha⁴

1 Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh
2 Research Chair of Online Dialogue and Cultural Communication, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 12372, Saudi Arabia
3 Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
4 Department of Computer Science, American International University-Bangladesh, Dhaka, 1229, Bangladesh

* Corresponding Author: Mejdl Safran. Email:

Computers, Materials & Continua 2025, 85(3), 5747-5763. https://doi.org/10.32604/cmc.2025.067589

Received 07 May 2025; Accepted 10 September 2025; Issue published 23 October 2025

Abstract

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.

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Keywords

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