An Efficient Privacy-aware Split Learning Framework for Satellite Communications

TL;DR

This paper introduces DTIP, a novel framework combining differential privacy and graph pruning to improve efficiency and privacy in satellite communication-based split learning, demonstrating significant accuracy and computational gains.

Contribution

The paper presents a new framework, DTIP, that enhances privacy, accuracy, and efficiency in satellite split learning by integrating differential privacy with graph neural network pruning.

Findings

DTIP maintains high accuracy (0.82 on Amazon2M, 0.85 on ArXiv)

Achieves 50% reduction in floating-point operations

Enhances privacy and computational efficiency in satellite networks

Abstract

In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP's efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.