Federated data selection strategically chooses a group of high-quality samples to train a global model, and it is promising to optimize the convergence and resource overhead of federated learning (FL). However, existing studies either fail to account for the dynamic importance of training samples or rely on external unbiased validation datasets. These shortcomings can compromise FL model performance, potentially complicating their application in real-world scenarios. In this paper, we propose a novel proxy-validated importance-aware federated sample selection framework, termed FedSelect. It employs a novel meta learning approach with a proxy validation dataset to select the most positively important clients and their most important samples, thereby accelerating the training process and optimizing FL model performance. To eliminate the dependency on external unbiased data, we present a momentum-based meta-margin function to discover influential samples as the proxy validation dataset, providing an adaptive reward for sample selection. Furthermore, we also develop an online meta model update strategy to guarantee the efficiency of FedSelect. Comprehensive experiments on four benchmark datasets demonstrate that FedSelect is superior in both effectiveness and efficiency, while maintaining strong scalability across diverse scenarios. The source code can be accessed at: https://github.com/nameyzhang/FedSelect.

Tabular data is abundant and crucial across both industry and academia. Federated learning (FL) offers a promising solution for the analysis of tabular data distributed across multiple organizations, without the need to share the privacy information of each client. Existing federated tabular data prediction methods optimize performance and privacy leakage under the completeness assumption of tabular data. They are not applicable in real-world scenarios that are struggling with missing values in tabular data. In this paper, we propose a novel federated prediction framework for incomplete tabular data, named DARN, which leverages the missing complementarity to directly optimize prediction performance without relying on the imputed values. It is especially beneficial when clients exhibit heterogeneity in missing data distributions, and the pairwise observed data are complementary. Specifically, each client trains a missing distribution learning model to capture the distribution of locally incomplete data. To assist in this, we present a missing-aware transformer block with a novel missing-aware attention mechanism to represent incomplete tabular data directly. The server calculates the personalized weights of the prediction models by combining missing complementary score and observed sample size score , thereby maximizing the utility of the available data. Extensive experiments on four publicly available real-world datasets demonstrate that DARN outperforms state-of-the-art methods with 25.80% improvement in both classification and regression tasks.

The recommendation of taxi pickup locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles provides technical support for it. However, existing recommendation methods do not model dynamic global positioning system information well and in real-time. In this article, we propose a spatio-temporal digraph convolutional network (STDCN) model. First, the pickup and drop-off locations are modeled into a directed spatio-temporal graph as input to the model. The correlation between each node is calculated as a unified edge weight based on the gray relational analysis. Then, the STDCN is used for dynamic spatio-temporal feature extraction. Finally, the edge-cloud collaboration framework is adopted to recommend local taxi pickup locations in real-time. The experimental results show that the proposed method is better than competing methods in terms of effectiveness and efficiency, and it shows good industrial conversion application prospects.