Federated object detection for defence and security applications using realistic unbalanced heterogeneous data distributions

There is often sensitive data in defence and security applications, making it difficult for organizations to share such data. This limits the training of artificial intelligence techniques, which typically require large, diverse datasets. Federated learning offers a solution by enabling organizations to collaboratively train models without sharing private data. However, existing research on federated learning often focuses on simple computer vision tasks, such as classification on balanced datasets, and rarely addresses more complex tasks involving realistic, heterogeneous data distributions, also known as non-IID (non-independent and identically distributed) data. In this work, we demonstrate a federated learning framework applied to various object detection tasks relevant to defence and security. These tasks are evaluated under different types of non-IID conditions, including quantity skew, label skew, and feature skew. The object detection tasks include number and symbol detection on UNO card corners, single-frame person and vehicle detection from an air-to-ground perspective using the VisDrone dataset, and small moving object detection in challenging environments. Experimental results show that federated models consistently outperform separately trained models in both IID and non-IID settings. In experiments involving the three types of skew, federated performance decreases as the data becomes more non-IID. However, our results still demonstrate the added benefit of federated training compared to separately trained models. These findings highlight the viability of federated object detection in real-world defence and security scenarios involving heterogeneous data.