Secure Progressive Federated Transfer Learning for Intelligent Satellite Imagery Analysis in Mega Constellations

Abstract

The emergence of mega satellite constellations has greatly advanced Earth observation, national security, and environmental monitoring. However, the massive volume and rapid acquisition rates of satellite imagery pose significant challenges in terms of processing speed, resource efficiency, and data privacy. To address these challenges, this paper proposes the Secure Progressive Federated Transfer Learning (SPFTL) framework, which integrates Federated Learning (FL) with Progressive Transfer Learning (PTL) and Selective Knowledge Distillation (SKD) to enable scalable, efficient, and privacy-preserving analysis of satellite imagery across distributed nodes. The SPFTL framework leverages a modular ResNet-50 architecture as the baseline pre-trained model for feature extraction, adapted for FL in a satellite constellation environment. The model undergoes layer-specific adaptation, considering the diverse computational and resource capabilities of satellite nodes. Nodes with limited resources refine earlier, less complex layers, while more capable nodes contribute to the fine-tuning of deeper layers that capture more complex, task-specific features. This hierarchical refinement strategy ensures optimal resource utilization and facilitates effective collaboration among nodes, without exceeding their individual computational limits. To further optimize performance and minimize bandwidth consumption, the SPFTL framework incorporates SKD, ensuring that only critical updates, essential for model improvement, are shared across the network. Additionally, differential privacy techniques are applied to the model updates, adding controlled noise to safeguard sensitive local data and guarantee privacy throughout the collaborative training process. These privacy-preserved updates are then securely aggregated on a central server to refine the global model. Comprehensive experiments on large-scale satellite imagery datasets validate the effectiveness of the SPFTL framework. The results demonstrate high classification accuracy (97.87%) with minimal communication overhead, showcasing its scalability and efficiency. The SPFTL framework outperforms traditional methods in terms of accuracy, computational efficiency, and resource utilization, providing a robust solution for secure, privacy-preserving, and distributed satellite imagery analysis in mega satellite constellations.

Keywords: Mega satellite constellationsImagery analysisFederated Learningtransfer learningdifferential privacyKnowledge distillation.