Blind Federated Learning without initial model

arXiv:2404.16180v1 Announce Type: new
Abstract: 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 …

abstract arxiv blind construction cs.lg data federated learning hospitals machine machine learning machine learning model paper privacy private data training type