When Secure Aggregation Falls Short: Achieving Long-Term Privacy in Asynchronous Federated Learning for LEO Satellite Networks

2025-08-18

AI Summary

In low-Earth-orbit (LEO) satellite-based asynchronous federated learning, secure aggregation faces two key challenges: intermittent satellite visibility hindering sustained client participation, and prolonged privacy leakage across multiple communication rounds. To address these, we propose LTP-FLEO—a novel framework featuring the first privacy-aware, visibility-driven satellite grouping mechanism, integrated with model freshness modeling and fairness-aware global aggregation to ensure continuous differential privacy across training rounds. Key technical contributions include: (i) enhanced asynchronous secure aggregation, (ii) model-age compensation, and (iii) a privacy-aware satellite partitioning algorithm. Theoretical analysis and simulations demonstrate that, under strict end-to-end privacy budget constraints, LTP-FLEO significantly improves model convergence speed and accuracy while enabling contribution-aware fair aggregation.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Secure aggregation is a common technique in federated learning (FL) for protecting data privacy from both curious internal entities (clients or server) and external adversaries (eavesdroppers). However, in dynamic and resource-constrained environments such as low Earth orbit (LEO) satellite networks, traditional secure aggregation methods fall short in two aspects: (1) they assume continuous client availability while LEO satellite visibility is intermittent and irregular; (2) they consider privacy in each communication round but have overlooked the possible privacy leakage through multiple rounds. To address these limitations, we propose LTP-FLEO, an asynchronous FL framework that preserves long-term privacy (LTP) for LEO satellite networks. LTP-FLEO introduces (i) privacy-aware satellite partitioning, which groups satellites based on their predictable visibility to the server and enforces joint participation; (ii) model age balancing, which mitigates the adverse impact of stale model updates; and (iii) fair global aggregation, which treats satellites of different visibility durations in an equitable manner. Theoretical analysis and empirical validation demonstrate that LTP-FLEO effectively safeguards both model and data privacy across multi-round training, promotes fairness in line with satellite contributions, accelerates global convergence, and achieves competitive model accuracy.

Problem

• Achieving long-term privacy in asynchronous federated learning for LEO satellite networks
• Addressing intermittent satellite visibility and irregular client availability issues
• Preventing privacy leakage across multiple rounds of federated learning

Innovation

• Asynchronous FL framework for intermittent connectivity
• Privacy-aware satellite grouping based on visibility
• Model age balancing and fair global aggregation