  Federated learning is an emerging machine learning approach that allows the
construction of a model between several participants who hold their own private
data. This method is secure and privacy-preserving, suitable for training a
machine learning model using sensitive data from different sources, such as
hospitals. In this paper, the authors propose two innovative methodologies for
Particle Swarm Optimisation-based federated learning of Fuzzy Cognitive Maps in
a privacy-preserving way. In addition, one relevant contribution this research
includes is the lack of an initial model in the federated learning process,
making it effectively blind. This proposal is tested with several open
datasets, improving both accuracy and precision.
