In this paper, we show how Federated Learning (FL) can be applied to
vehicular use-cases in which we seek to classify obstacles, irregularities and
pavement types on roads. Our proposed framework utilizes FL and TabNet, a
state-of-the-art neural network for tabular data. We are the first to
demonstrate how TabNet can be integrated with FL. Moreover, we achieve a
maximum test accuracy of 93.6%. Finally, we reason why FL is a suitable concept
for this data set.

本文研究了如何将联邦学习应用于车辆使用案例中，目标是对道路上的障碍物、异常情况和路面类型进行分类。我们提出的框架利用了联邦学习和 TabNet，这是一种用于表格数据的先进神经网络。我们是第一个展示 TabNet 如何与联邦学习集成的研究，并且我们取得了最高 93.6% 的测试准确率。最后，我们论证了联邦学习是该数据集的一个合适概念。

基于 TabNet 的面向车辆应用的表格数据联邦学习

Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

Autoencoders are popular neural networks that are able to compress high
dimensional data to extract relevant latent information. TabNet is a
state-of-the-art neural network model designed for tabular data that utilizes
an autoencoder architecture for training. Vertical Federated Learning (VFL) is
an emerging distributed machine learning paradigm that allows multiple parties
to train a model collaboratively on vertically partitioned data while
maintaining data privacy. The existing design of training autoencoders in VFL
is to train a separate autoencoder in each participant and aggregate the latent
representation later. This design could potentially break important
correlations between feature data of participating parties, as each autoencoder
is trained on locally available features while disregarding the features of
others. In addition, traditional autoencoders are not specifically designed for
tabular data, which is ubiquitous in VFL settings. Moreover, the impact of
client failures during training on the model robustness is under-researched in
the VFL scene. In this paper, we propose TabVFL, a distributed framework
designed to improve latent representation learning using the joint features of
participants. The framework (i) preserves privacy by mitigating potential data
leakage with the addition of a fully-connected layer, (ii) conserves feature
correlations by learning one latent representation vector, and (iii) provides
enhanced robustness against client failures during training phase. Extensive
experiments on five classification datasets show that TabVFL can outperform the
prior work design, with 26.12% of improvement on f1-score.

TabVFL 是一个分布式框架，通过使用参与者的联合特征，改善了潜在表示学习的效果，保护了数据隐私，保留了特征相关性，并在训练过程中提供了增强的鲁棒性。

TabVFL：垂直联邦学习中潜在表示的改进

TabVFL: Improving Latent Representation in Vertical Federated Learning

Water crisis is a crucial concern around the globe. Appropriate and timely
maintenance of water pumps in drought-hit countries is vital for communities
relying on the well. In this paper, we analyze and apply a sequential attentive
deep neural architecture, TabNet, for predicting water pump repair status in
Tanzania. The model combines the valuable benefits of tree-based algorithms and
neural networks, enabling end-to-end training, model interpretability, sparse
feature selection, and efficient learning on tabular data. Finally, we compare
the performance of TabNet with popular gradient tree-boosting algorithms like
XGBoost, LightGBM,CatBoost, and demonstrate how we can further uplift the
performance by choosing focal loss as the objective function while training on
imbalanced data.

本研究旨在利用一种串联式注意力深度神经结构 TabNet，预测干旱国家坦桑尼亚水泵的维修情况，并比较其与 XGBoost、LightGBM、CatBoost 等梯度树提升算法的性能差异。

使用关注式表格学习预测水泵状态

Pump It Up: Predict Water Pump Status using Attentive Tabular Learning

We propose a novel high-performance and interpretable canonical deep tabular
data learning architecture, TabNet. TabNet uses sequential attention to choose
which features to reason from at each decision step, enabling interpretability
and more efficient learning as the learning capacity is used for the most
salient features. We demonstrate that TabNet outperforms other neural network
and decision tree variants on a wide range of non-performance-saturated tabular
datasets and yields interpretable feature attributions plus insights into the
global model behavior. Finally, for the first time to our knowledge, we
demonstrate self-supervised learning for tabular data, significantly improving
performance with unsupervised representation learning when unlabeled data is
abundant.

本研究提出了一种新的、高性能和可解释的深度表格数据学习架构 TabNet，采用顺序注意方法在每个决策步骤中选择推理特征，实现了可解释性和更高效的学习。我们证明，在各种非性能饱和表格数据集上，TabNet 胜过其他神经网络和决策树变体，并提供了可解释的特征归因和对全局模型行为的深入认识。最后，我们首次在表格数据上展示了自监督学习，在未标记的数据丰富情况下显著提高了性能。