Due to the strong contamination， high noise level， excessive exposure， and other problems in flame images from domestic Municipal Solid Waste Incineration （MSWI） processes， traditional target recognition methods are difficult to apply to them. Therefore， an MSWI incineration image classification framework — SAswin with Multilayer Preprocessing Network （SAswin-MPNet） was proposed. Firstly， a Transformer-based Hybrid Attention Super-Resolution Transformer （HASRT） module was designed to perform super-resolution reconstruction to the images. Secondly， a Practical Exposure Correction （PEC） module was introduced to correct the exposure of high-resolution MSWI images， thereby obtaining multilayer preprocessed data. Additionally， a validation algorithm was designed to compare and test the preprocessed images and the originals， and the images meeting a validation threshold were used to replace the originals， thereby obtaining a multilayer preprocessed dataset. Finally， an SAswin classification network was constructed to recognize incineration states. Experimental results based on actual operational data from an MSWI power plant comparing with ResNet-34， ResNet-50， ConvNeXt， ViT （Vision Transformer）， Swin-T （Swin-Tiny）， and EVA-02 （Enhanced Visual Assistant-02） show that SAswin-MPNet achieves the optimal MSWI image incineration state recognition accuracy and F1-score.

With the rapid development of artificial intelligence， the risk of user privacy disclosure is becoming serious increasingly. Differential privacy is a key privacy protection technology， which prevents personal information leakage by introducing noise into data， while Federated Learning （FL） allows joint training of models without exchanging data to protect data security. In recent years， differential privacy technology and FL are used together to give full play of their respective advantages： differential privacy ensures privacy protection in the process of data use， while FL improves the generalization ability and efficiency of the model through distributed training. Aiming at the privacy security problem of FL， firstly， the latest research progress of FL based on differential privacy was summarized and compared systematically， including different differential privacy mechanisms， FL algorithms and application scenarios. Secondly， special attention was paid to application approaches of differential privacy in FL， including data aggregation， gradient descent， and model training， and the advantages and disadvantages of various technologies were analyzed. Finally， the existing challenges and development directions of this field were summarized in detail.

This survey summarized the studies on text sentiment analysis in the view of granularity from the following five aspects: sentiment word extraction, sentiment corpus and dictionary construction, entity and opinion holders analysis,document level sentiment analysis, and text sentiment analysis applications. It pointed out that the current sentiment analysis system cannot gain high precision. Further research should focus on: widely and appropriately applying study achievement of natural language processing to text sentiment analysis; finding and choosing suitable features and algorithms in text sentiment classifications; utilizing the existing language tools and relevant resources in fast building standard language tools and resources and applying them.