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

TL;DR

This paper introduces LTP-FLEO, an asynchronous federated learning framework designed for LEO satellite networks that ensures long-term privacy, fairness, and efficient convergence despite intermittent satellite visibility and multiple-round privacy risks.

Contribution

LTP-FLEO is the first framework to address long-term privacy and fairness in asynchronous FL for LEO satellite networks, incorporating satellite partitioning, model age balancing, and fair aggregation.

Findings

Effectively safeguards model and data privacy across multiple rounds.

Promotes fairness among satellites with different visibility durations.

Accelerates global convergence and maintains competitive accuracy.

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.