Abstract
Federated Learning (FL) enables collaborative model training while keeping training data localized, allowing us to preserve privacy in various domains including remote sensing. However, recent studies show that FL models may still leak sensitive information through their outputs, motivating the need for rigorous privacy evaluation. In this paper, we leverage membership inference attacks (MIA) as a quantitative privacy measurement framework for FL applied to remote sensing image classification. We evaluate multiple black-box MIA techniques, including entropy-based attacks, modified entropy attacks, and the likelihood ratio attack, across different FL algorithms and communication strategies. Experiments conducted on two public scene classification datasets demonstrate that MIA effectively reveals privacy leakage not captured by accuracy alone. Our results show that communication-efficient FL strategies reduce MIA success rates while maintaining competitive performance. These findings confirm MIA as a practical metric and highlight the importance of integrating privacy measurement into FL system design for remote sensing applications.
Abstract (translated)
联邦学习（FL）能够在保持训练数据本地化的同时进行协作模型训练，从而在包括远程感应在内的多个领域中保护隐私。然而，近期的研究表明，联邦学习的模型仍然可能通过其输出泄露敏感信息，这促使了严格的隐私评估需求的出现。本文利用成员推断攻击（MIA）作为量化联邦学习应用于遥感图像分类中的隐私测量框架。我们评估了多种黑盒MIA技术，包括基于熵的攻击、修改后的熵攻击以及似然比攻击，在不同的FL算法和通信策略下进行测试。在两个公开场景分类数据集上进行的实验表明，MIA能够有效揭示超出准确度衡量之外的隐私泄露情况。我们的结果显示，通信效率较高的联邦学习策略能够在保持竞争力的同时降低MIA的成功率。这些发现确认了MIA作为实用度量标准的重要性，并强调了将隐私评估整合进远程感应应用中FL系统设计中的重要性。
URL
https://arxiv.org/abs/2601.06200
