Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems

• URL: http://arxiv.org/abs/2509.12222v1
• Date: Fri, 05 Sep 2025 03:33:42 GMT
• Title: Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems
• Abstract summary: Large-scale low-Earth-orbit (LEO) satellite systems are increasingly valued for their ability to enable rapid and wide-area data exchange.<n>Due to privacy concerns and regulatory constraints, raw data collected at remote clients cannot be centrally aggregated.<n>Federated learning offers a privacy-preserving alternative by training local models on distributed devices and exchanging only model parameters.<n>We propose a discrete temporal graph-based on-demand scheduling framework that dynamically allocates communication resources to accelerate federated learning.
• Score: 57.692181589325116
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large-scale low-Earth-orbit (LEO) satellite systems are increasingly valued for their ability to enable rapid and wide-area data exchange, thereby facilitating the collaborative training of artificial intelligence (AI) models across geographically distributed regions. Due to privacy concerns and regulatory constraints, raw data collected at remote clients cannot be centrally aggregated, posing a major obstacle to traditional AI training methods. Federated learning offers a privacy-preserving alternative by training local models on distributed devices and exchanging only model parameters. However, the dynamic topology and limited bandwidth of satellite systems will hinder timely parameter aggregation and distribution, resulting in prolonged training times. To address this challenge, we investigate the problem of scheduling federated learning over satellite networks and identify key bottlenecks that impact the overall duration of each training round. We propose a discrete temporal graph-based on-demand scheduling framework that dynamically allocates communication resources to accelerate federated learning. Simulation results demonstrate that the proposed approach achieves significant performance gains over traditional statistical multiplexing-based model exchange strategies, reducing overall round times by 14.20% to 41.48%. Moreover, the acceleration effect becomes more pronounced for larger models and higher numbers of clients, highlighting the scalability of the proposed approach.

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