Mutual Information-Based Feature Selection for Federated Learning Environments

Due to the growth of Internet of Things devices, the dimensionality of the data has only increased. These devices generate a large amount of data which, after being processed, is one of the main sources of information for machine learning systems. However, only a small part of this data is truly relevant. Understanding the relevance of this data allows us to process it at the edge of the network and improve the performance of Artificial Intelligence systems. On the one hand, feature selection is one of the most common approaches to reducing irrelevant features. On the other hand, federated learning allows the exploitation of such data by machine learning models without the need to exchange raw data. Therefore, in this paper we present modifications to two widely utilised feature selection algorithms based on the Mutual Information metric to enable them to work in a federated environment. The experimental process conducted on several datasets and data distributions demonstrates the capability of our modifications to operate in such an environment without a loss of information. This enhances execution time and leverages specific particularities of these environments, such as increased security.