FMLFS: A Federated Multi-Label Feature Selection Based on Information Theory in IoT Environment

TL;DR

This paper presents FMLFS, a novel federated multi-label feature selection method for IoT environments that improves classifier performance by reducing data dimensionality while considering communication costs.

Contribution

FMLFS is the first distributed multi-label feature selection approach tailored for IoT, utilizing mutual information and Pareto optimization for effective feature relevance and redundancy management.

Findings

FMLFS outperforms five existing methods in accuracy and efficiency.

It reduces communication costs in federated learning scenarios.

Demonstrates effectiveness on three real-world IoT datasets.

Abstract

In certain emerging applications such as health monitoring wearable and traffic monitoring systems, Internet-of-Things (IoT) devices generate or collect a huge amount of multi-label datasets. Within these datasets, each instance is linked to a set of labels. The presence of noisy, redundant, or irrelevant features in these datasets, along with the curse of dimensionality, poses challenges for multi-label classifiers. Feature selection (FS) proves to be an effective strategy in enhancing classifier performance and addressing these challenges. Yet, there is currently no existing distributed multi-label FS method documented in the literature that is suitable for distributed multi-label datasets within IoT environments. This paper introduces FMLFS, the first federated multi-label feature selection method. Here, mutual information between features and labels serves as the relevancy metric, while the correlation distance between features, derived from mutual information and joint entropy, is utilized as the redundancy measure. Following aggregation of these metrics on the edge server and employing Pareto-based bi-objective and crowding distance strategies, the sorted features are subsequently sent back to the IoT devices. The proposed method is evaluated through two scenarios: 1) transmitting reduced-size datasets to the edge server for centralized classifier usage, and 2) employing federated learning with reduced-size datasets. Evaluation across three metrics - performance, time complexity, and communication cost - demonstrates that FMLFS outperforms five other comparable methods in the literature and provides a good trade-off on three real-world datasets.