Federated learning-based automotive navigation has recently received considerable attention, as it can potentially address the issue of weak global positioning system (GPS) signals under severe blockages, such as in downtowns and tunnels. Specifically, the data-driven navigation framework combines the position estimation offered by the high-sampling inertial measurement units and the position calibration provided by the low-sampling GPS signals. Despite its promise, the privacy preservation and flexibility of the participating users in the federated learning process are still problematic. To address these challenges, in this article, we propose an efficient, flexible, and privacy-preserving model aggregation scheme under a federated learning-based navigation framework named FedLoc. Specifically, our proposed scheme efficiently protects the locally trained model updates, flexibly supports the fluctuation of participants, and is robust against unregistered malicious users by exploiting a homomorphic threshold cryptosystem, together with the bounded Laplace mechanism and the skip list. We perform a detailed security analysis to demonstrate the security properties in terms of privacy preservation and dishonest user detection. In addition, we evaluate and compare the computational efficiency with two traditional schemes, and the simulation results show that our scheme greatly improves the computational efficiency during participant fluctuation. To validate the effectiveness of our scheme, we also show that only part of the model update is excluded from aggregation in the case of a dishonest user.

中文翻译：

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车辆雾中基于联合学习的导航的隐私保护聚合


基于联邦学习的汽车导航最近受到了相当大的关注，因为它可以潜在地解决严重堵塞（例如在市中心和隧道）下全球定位系统（GPS）信号弱的问题。具体来说，数据驱动的导航框架结合了高采样惯性测量单元提供的位置估计和低采样GPS信号提供的位置校准。尽管做出了承诺，但联邦学习过程中参与用户的隐私保护和灵活性仍然存在问题。为了应对这些挑战，在本文中，我们在名为 FedLoc 的基于联邦学习的导航框架下提出了一种高效、灵活且保护隐私的模型聚合方案。具体来说，我们提出的方案通过利用同态阈值密码系统以及有界拉普拉斯机制和跳跃列表，有效保护本地训练的模型更新，灵活支持参与者的波动，并且对未注册的恶意用户具有鲁棒性。我们进行了详细的安全分析，以展示隐私保护和不诚实用户检测方面的安全属性。此外，我们对两种传统方案的计算效率进行了评估和比较，仿真结果表明，我们的方案极大地提高了参与者波动期间的计算效率。为了验证我们方案的有效性，我们还表明，在不诚实用户的情况下，只有部分模型更新被排除在聚合之外。