In this paper we propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. We address the challenges of data privacy and heterogeneity in autonomous vehicle environments by proposing a personalized Federated Learning scenario that allows selective sharing of model parameters. Our method implements a coarse-to-fine approach, where clients share only the coarse feature extractors while keeping fine-grained features specific to local environments. We evaluate our approach against traditional centralized and single-client training schemes using the KITTI dataset combined with satellite imagery. Results demonstrate that our federated CVGL method achieves performance close to centralized training while maintaining data privacy. The proposed partial model sharing strategy shows comparable or slightly better performance than classical FL, offering significant reduced communication overhead without sacrificing accuracy. Our work contributes to more robust and privacy-preserving localization systems for autonomous vehicles operating in diverse environments

翻译：本文提出了一种将联邦学习（FL）与跨视角图像地理定位（CVGL）技术相结合的方法。针对自动驾驶环境中数据隐私和异构性的挑战，我们提出了一种个性化的联邦学习方案，允许选择性共享模型参数。我们的方法采用由粗到精的策略：客户端仅共享粗粒度特征提取器，而保留针对本地环境的细粒度特征。我们使用KITTI数据集结合卫星影像，将所提方法与传统的集中式及单客户端训练方案进行对比评估。结果表明，我们的联邦CVGL方法在保持数据隐私的同时，达到了接近集中式训练的性能。所提出的部分模型共享策略相较于经典联邦学习表现出相当或略优的性能，在保证精度的同时显著降低了通信开销。本研究为在不同环境中运行的自动驾驶车辆提供了更鲁棒且保护隐私的定位系统。