Federated learning has received a great deal of research attention recently,with privacy protection becoming a key factor in the development of artificial intelligence. Federated learning is a special kind of distributed learning framework, which allows multiple users to participate in model training while ensuring that their privacy is not compromised; however, this paradigm is still vulnerable to security and privacy threats from various attackers. This paper focuses on the security and privacy threats related to federated learning. First, we analyse the current research and development status of federated learning through use of the CiteSpace literature search tool. Next, we describe the basic concepts and threat models, and then analyse the security and privacy vulnerabilities within current federated learning architectures. Finally, the directions of development in this area are further discussed in the context of current advanced defence solutions, for which we provide a summary and comparison.

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in credit card fraud detection, but these methods are often have difficulty in demonstrating their effectiveness when faced with unknown attack patterns. In this paper, a new Unsupervised Attentional Anomaly Detection Network-based Credit Card Fraud Detection framework (UAAD-FDNet) is proposed. Among them, fraudulent transactions are regarded as abnormal samples, and autoencoders with Feature Attention and GANs are used to effectively separate them from massive transaction data. Extensive experimental results on Kaggle Credit Card Fraud Detection Dataset and IEEE-CIS Fraud Detection Dataset demonstrate that the proposed method outperforms existing fraud detection methods.

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature selection algorithm and an optimized loss function is proposed, which can be applied in the field of credit evaluation and improve the risk management ability of financial institutions. Firstly, the Boruta algorithm is embedded for feature selection, which can effectively reduce the data dimension and noise and improve the model’s capacity for generalization by automatically identifying and screening out features that are highly correlated with target variables. Then, the GHM loss function is incorporated into the XGBoost model to tackle the issue of skewed sample distribution, which is common in classification, and further improve the classification and prediction performance of the model. The comparative experiments on four large datasets demonstrate that the proposed method is superior to the existing mainstream methods and can effectively extract features and handle the problem of imbalanced samples.

The severity of the global warming issue emphasizes the critical importance of utilizing carbon satellite data to estimate groundlevel carbon dioxide emissions. However, existing reviews have not kept pace with the latest research developments. Therefore, this paper provides an overview of relevant work in the global carbon emissions field to address this knowledge gap. Through visual analysis using Citespace software, the paper outlines two methods for quantifying carbon dioxide: ground-level observations and satellite remote sensing. Despite the unique advantages of ground-level observations, satellite remote sensing is crucial for its extensive spatial coverage and long-term continuity in understanding carbon cycling, drawing significant attention. Additionally, the paper integrates the application of machine learning in the carbon emissions field, dividing it into two parts: direct estimation based on ground emission inventory data and estimation of ground-level carbon emissions based on carbon satellite data. This innovative approach combines satellite observational data with ground data to accurately estimate the current ground-level carbon emissions with robust spatial distribution characteristics.