Efficient Military Aircraft Target Detection Model Based on Federated Meta-Learning

Military aircraft detection holds critical significance in defense operations, ensuring accurate identification and classification of aircraft for effective decision-making. However, existing methodologies face challenges due to disparate data collection, limited data availability, and the complexity of aggregating remote datasets. In response to these challenges, we propose a novel approach FedMATD, utilizing Federated Meta-Learning techniques to address the difficulties in data collection. To figure out the limitation in the scale of dataset, we integrate Federated Meta-Learning with a strategy focusing on training with small sample sizes. This innovative fusion aims to enhance target detection accuracy by leveraging the advantages of federated learning while mitigating the limitations posed by insufficient data quantities and remote data aggregation complexities. Our proposed method is evaluated using one open-source dataset, and our results demonstrate that FedMATD achieves a better level.