通过局部共享粗特征，实现跨视角定位的隐私保护联邦学习
Personalized Federated Learning for Cross-view Geo-localization
- 客户端仅共享粗粒度特征，保留本地细粒度特征
- 性能接近集中式训练，通信开销显著降低
- 适合自动驾驶多场景下隐私敏感的定位系统
本文提出一种结合联邦学习（FL）与跨视角图像地理定位（CVGL）的方法，解决自动驾驶环境中的数据隐私与异构性问题。设计个性化联邦学习框架，允许选择性共享模型参数。采用从粗到细的策略，客户端仅共享粗粒度特征提取器，本地保留细粒度特征以适应特定环境。在结合卫星图像的KITTI数据集上评估，结果表明该方法性能接近集中式训练，同时保障数据隐私。部分参数共享策略相比传统联邦学习表现相当或更优，通信开销大幅减少且精度无损失。本工作推动了多样化环境下更鲁棒、隐私友好的自动驾驶定位系统发展。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。