Improving Federated Learning UAV Urban Object Detection System via Data Heterogeneity Mitigation

Federated learning (FL)-based object detection systems provide many advantages, such as efficiency and privacy. However, performance degradation due to the data heterogeneity issue remains a critical yet often overlooked challenge in recent FL research. In this paper, we address the data heterogeneity issue by introducing model contrastive loss, which significantly improves performance compared to baseline methods. In addition, focal loss is applied to further enhance the prediction accuracy on minority-class objects. Experimental results demonstrate the effectiveness of the proposed federated training framework, achieving approximately 20% improvement in mean average precision over the baseline FedAvg. Furthermore, extensive ablation studies on different hyperparameters in the model contrastive loss are conducted, providing deeper insights into the impact of parameter selection.