Exploiting Federated Learning Approach for Efficient Bandwidth Utilization in IoT-Based Surveillance System

As Internet of Things (IoT)-based surveillance devices expand, maintaining data privacy and bandwidth efficiency has emerged as a crucial concern. Security vulnerabilities, bandwidth congestion, and latency are common problems with traditional cloud systems. This paper uses edge computing, YOLOv9 object detection, and H.265 video encoding in a surveillance architecture based on federated learning (FL) to address these problems. The proposed system retains and compresses only motion-based frames before transmitting them to the central server, considerably minimizing duplicate data transfer. The FL model is collaboratively trained across three edge clients without sharing raw video, thereby preserving privacy. After 200 rounds of communication, the global model achieved a mean Average Precision (mAP@50) of 94.6%, a precision of 91.5%, a recall of 88.4%, and an F1-score of 89.6%. Furthermore, the system reduced an experimental video size from 32 MB to 2 MB and duration from 11 seconds to 5 seconds by filtering out static frames. Additionally, field experiments on several video footages confirmed the robustness of the proposed technique. For page limitations, only one sample video result is described in detail in the main text and summarizes the performance over all tested videos in a comparison table. These results validate that the proposed combination of FL, intelligent frame filtering, and H.265 compression leads to a scalable, bandwidth-efficient, and privacy-preserving IoT surveillance.