  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.
