Personalized Federated Learning for Cross-view Geo-localization

TL;DR

This paper introduces a personalized federated learning approach for cross-view geo-localization in autonomous vehicles, balancing privacy, communication efficiency, and localization accuracy across diverse environments.

Contribution

It proposes a novel partial model sharing federated learning framework tailored for cross-view geo-localization, addressing data privacy and heterogeneity challenges.

Findings

Achieves near-centralized performance in geo-localization tasks.

Reduces communication overhead compared to traditional federated learning.

Maintains high accuracy while preserving data privacy.

Abstract

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