Under horizontal federated learning (HFL) in the Internet of Things (IoT) scenarios, different user data sets have significant similarities on the feature spaces, the final goal is to build a high-performance global model. However, not all features are great contributors when training the global HFL model, some features even impair the HFL. Besides, the curse of dimension will delay the training time and cause more energy consumption (EC). In this case, it is critical to remove irrelevant features from the local and select the useful overlapping features from a federated global perspective. In addition, the uncertainty of data being labeled and the nonindependent and identically distributed (non-IID) client data should also consider. This article introduces an unsupervised federated feature selection approach (named FSHFL) for HFL in IoT networks. First, a feature relevance outlier detection method is applied to the HFL participants to remove the useless features, which combines with the improved one-class support vector machine. Besides, a feature relevance hierarchical clustering (FRHC) algorithm is proposed for HFL overlapping feature selection. Experiment results on four IoT data sets show that the proposed methods can select better-federated feature sets among HFL participants, thus improving the performance of the HFL system. Specifically, the global model accuracy improves up to 1.68% since fewer irrelevant features. Moreover, FSHFL can lower the average training time as high as 6.9%. Finally, when the global model gets the same test accuracy, FSHFL can decrease the average EC of training the model by approximately 2.85% compared to federated average and roughly 68.39% compared to Fed-SGD.

中文翻译：

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物联网水平联邦学习的联邦特征选择


在物联网（IoT）场景中的水平联邦学习（HFL）下，不同的用户数据集在特征空间上具有显着的相似性，最终目标是构建高性能的全局模型。然而，并不是所有的特征在训练全局 HFL 模型时都有很大的贡献，有些特征甚至会损害 HFL。此外，维数灾难会延迟训练时间并导致更多的能量消耗（EC）。在这种情况下，从局部去除不相关的特征并从联合的全局角度选择有用的重叠特征至关重要。此外，还应考虑标记数据的不确定性以及非独立同分布（non-IID）客户端数据。本文介绍了一种适用于物联网网络中 HFL 的无监督联合特征选择方法（称为 FSHFL）。首先，对HFL参与者应用特征相关性异常值检测方法，去除无用特征，并与改进的一类支持向量机相结合。此外，还提出了一种用于 HFL 重叠特征选择的特征相关性分层聚类（FRHC）算法。在四个物联网数据集上的实验结果表明，所提出的方法可以在 HFL 参与者之间选择更好的联合特征集，从而提高 HFL 系统的性能。具体来说，由于不相关特征的减少，全局模型精度提高了 1.68%。此外，FSHFL 可以将平均训练时间降低高达 6.9%。最后，当全局模型获得相同的测试精度时，FSHFL 可以使训练模型的平均 EC 比联邦平均值降低约 2.85%，与 Fed-SGD 相比降低约 68.39%。