  The preservation, monitoring, and control of water resources has been a major
challenge in recent decades. Water resources must be constantly monitored to
know the contamination levels of water. To meet this objective, this paper
proposes a water monitoring system using autonomous surface vehicles, equipped
with water quality sensors, based on a multimodal particle swarm optimization,
and the federated learning technique, with Gaussian process as a surrogate
model, the AquaFeL-PSO algorithm. The proposed monitoring system has two
phases, the exploration phase and the exploitation phase. In the exploration
phase, the vehicles examine the surface of the water resource, and with the
data acquired by the water quality sensors, a first water quality model is
estimated in the central server. In the exploitation phase, the area is divided
into action zones using the model estimated in the exploration phase for a
better exploitation of the contamination zones. To obtain the final water
quality model of the water resource, the models obtained in both phases are
combined. The results demonstrate the efficiency of the proposed path planner
in obtaining water quality models of the pollution zones, with a 14$\%$
improvement over the other path planners compared, and the entire water
resource, obtaining a 400$\%$ better model, as well as in detecting pollution
peaks, the improvement in this case study is 4,000$\%$. It was also proven that
the results obtained by applying the federated learning technique are very
similar to the results of a centralized system.
