A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research
This addresses privacy concerns in collaborative cancer research by enabling distributed learning while maintaining data compliance.
The authors developed a privacy-preserving distributed learning method for Particle Swarm Optimization-based Fuzzy Cognitive Maps and applied it to cancer detection, showing improved model performance through Federated Learning with results comparable to existing literature.
Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.