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.

Multirobot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study addresses the challenge of deploying a multirobot team for optimal coverage in environments where the density distribution, describing areas of interest, is unknown and changes over time. We propose a fully distributed control strategy that uses Gaussian processes (GPs) to model the spatial field and balance the tradeoff between learning the field and optimally covering it. Unlike existing approaches, we address a more realistic scenario by handling time-varying spatial fields, where theexploration-exploitationtradeoff is dynamically adjusted over time. Each robot operates locally, using only its own collected data and the information shared by the neighboring robots. To address the computational limits of GPs, the algorithm efficiently manages the volume of data by selecting only the most relevant samples for the process estimation. The performance of the proposed algorithm is evaluated through several simulations and experiments, incorporating real-world data phenomena to validate its effectiveness.