A Novel Federated Learning Approach to Enable Distributed and Collaborative Genetic Programming

The combination of genetic programming with federated learning could solve the computational distribution while promoting a collaborative learning environment. This paper proposes a federated learning configuration that enables the use of genetic programming for its global model. In addition, this paper also proposes a new aggregation algorithm that enables the collaborative evolution of genetic programming individuals in federated learning. The case study uses flexible genetic programming, an existing and successful algorithm for image classification, integrated into a federated learning framework. The results show that the use of genetic programming with federated learning achieved a classification error rate of 1.67%, better than the scenario without federated learning, that had an error rate of 3.33%, considering a configuration with three clients with different datasets each.