Personalized Federated Learning for Cross-view Geo-localization
This work addresses privacy-preserving localization for autonomous vehicles, but it is incremental as it builds on existing federated learning and geo-localization techniques.
The paper tackles the problem of data privacy and heterogeneity in cross-view geo-localization for autonomous vehicles by proposing a personalized federated learning method with selective parameter sharing, achieving performance close to centralized training while reducing communication overhead.
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