Privacy-preserving distributed learning based on genetic algorithms and artificial neural networks

Abstract

In recent years, Machine Learning (ML) has witnessed a great increase of storage capacity of computer systems and an enormous growth of available information to work with thanks to the WWW. This has raised an opportunity for new real life applications of ML methods and also new cutting-edge ML challenges like: tackle with massive databases, Distributed Learning and Privacy-preserving Classification. In this paper a new method capable of dealing with this three problems is presented. The method is based on Artificial Neural Networks with incremental learning and Genetic Algorithms. As supported by the experimental results, this method is able to fastly obtain an accurate model based on the information of distributed databases without exchanging any data during the training process, without degrading its classification accuracy when compared with other non-distributed classical ML methods. This makes the proposed method very efficient and adequate for Privacy-Preserving Learning applications. © 2009 Springer Berlin Heidelberg.