Online Multi-Robot Federated Learning for Distributed Coverage Control of Unknown Spatial Processes

Abstract

Distributed multi-robot teams are increasingly used for optimal coverage of domains with unknown density distributions, often modeled with Gaussian Processes (GPs). However, current methods rely on data sharing, raising privacy concerns and computational issues. We propose a Federated Learning (FL) approach that enables collaborative training of GP models without sharing raw data. To enhance scalability and efficiency, we introduce a filtering strategy that selects relevant data samples, minimizing computational load. Realistic simulations emulating real world scenarios demonstrate the effectiveness of our method in achieving robust environmental estimates with minimal data sharing and reduced complexity.