Wrapper-Based Federated Feature Selection for IoT Environments

Novel Internet of Things (IoT) applications have emerged as enabling technologies for the smart city initiative. IoT devices collect or produce huge multi-modal data that is either processed on the edge or sent to a central cloud for processing. The collected data sets are pre-processed by methods known as “feature selection”, to remove redundant, irrelevant, or noisy features. Feature selection will help with improving the results achieved by the learning method as well as reducing the computational complexity of the model. The goal is to select the most informative features of data and only transmit the selected features to the edge/cloud servers for further processing. This leads to smaller costs for data transmission to the servers. In this paper, a novel wrapper-based federated feature selection (FFS) algorithm is proposed, where IoT devices collaborate to select the most informative features without sharing their local data sets. The proposed FFS algorithm uses binary gravitational search algorithm (BGSA) in a federated and collaborative manner to select a small enough subset of informative attributes and provide an improved trade-off between communication cost and learning accuracy. Our experimental results on three data sets including MNIST, Fashion-MNIST, and MAV demonstrate that the proposed BGSAFFS method can in average remove more than 50% of features without losing information. The obtained results prove the effectiveness of the proposed method in achieving a good trade-off between accuracy and communication cost in comparison to other state-of-the-art feature selection methods as well as a no-feature selection baseline.

新颖的物联网应用程序已成为智慧城市计划的启用技术。通过称为“特征选择”的方法进行处理，以删除冗余，无关紧要或噪声特征。降低模型的计算复杂性提出了一种基于包装的新型联合特征选择（FFS）算法，在其中，IoT设备协作以选择最有用的功能，而无需共享其本地数据集。引力搜索算法（BGSA）以联合和协作的方式选择了足够小的信息属性，并在包括MNIST，Fashion-MNIST和包括MNIST的三个数据集之间提供了沟通成本和学习精度之间的折衷。 MAV证明，所提出的BGSAFFS方法平均可以删除50％以上的功能，而不会丢失信息。与其他最先进的特征选择方法相比，在准确性和沟通成本之间取决于良好的权衡方面的拟议方法以及不合作选择的基线。