Federated Edge Intelligence: A Collaborative Learning Framework for Multi-Object Detection on Mobile Platforms

Abstract

Real-time multi-object detection on smartphones requires a careful balance of accuracy, latency, energy efficiency, and data privacy. We introduce FedEdgeDetect, a unified framework that combines federated learning with edge-assisted inference to address these challenges holistically. The system incorporates a hardware-aware YOLOv5s variant with lightweight attention modules for efficient on-device execution. A capability-clustered federated training protocol is designed to ensure privacy through differential noise injection and secure aggregation, while reducing communication overhead. At inference time, a dynamic controller adaptively partitions computation between the device and edge, optimizing for real-time performance and energy consumption. Experiments across diverse datasets and devices demonstrate that FedEdgeDetect consistently improves detection accuracy, accelerates inference, enhances energy efficiency, and enforces strong privacy guarantees, outperforming existing mobile detection baselines.