Tuning Detection Transformer with Device-to-Device Communication for Mission-Oriented Object Detection

Object detection is vital for various applications like autonomous vehicles and surveillance. Mission-oriented applications, such as retail, manufacturing, agriculture, healthcare, and robotics, require additional tuning for specific target images, often containing privacy-sensitive data unsuitable for cloud storage. With the deployment of AI chips for edge devices, training object detection models on-device becomes feasible, allowing collaborative model training among multiple devices via device-to-device communication. This paper proposes Wireless Ad Hoc Federated Learning for Detection Transformers (DETR), introducing three parameter-exchange methods: full-parameter exchange (FPE), transformer-layer exchange (TLE), and head ex-change (HE) for the distributed environment. This paper analyzes their impact on prediction accuracy and communication load. Experiments demonstrate that WAFL-DETR-TLE outperforms others, covering both IID and non-IID label distribution scenarios across various network topologies.