SatelliteEdgeNet: Secure Edge-Aware Federated Learning for Satellite Imagery

Mega satellite constellations provide unprecedented capabilities for high-resolution Earth observation, supporting applications such as environmental monitoring, urban mapping, and disaster management. However, the limited onboard power, computational resources, and intermittent connectivity of satellites pose significant challenges for real-time image analysis. This paper introduces SatelliteEdgeNet, a secure, efficient, and scalable framework designed for distributed satellite imagery analysis. The framework combines a lightweight, multi-scale Convolutional Neural Network (CNN) optimized for low-power space devices with a privacy-preserving Federated Learning (FL) scheme. Each satellite performs local model training on its high-resolution imagery, employing advanced preprocessing. Differential privacy is applied to both local gradient updates and global aggregation to ensure the confidentiality of sensitive spatial information. To optimize performance, SatelliteEdgeNet incorporates gradient clipping, structured pruning, and 8-bit quantization, reducing communication overhead and computational demand while maintaining high model accuracy. Experimental results on real-world satellite imagery demonstrate that the framework achieves up to 99.65% classification accuracy, robustly handles dynamic satellite availability, and efficiently balances computation, bandwidth, and privacy. SatelliteEdgeNet provides a practical, secure, and resource-aware solution for next-generation satellite constellations requiring real-time, collaborative, and privacy-preserving image analysis.