# Improved Asynchronous Federated Learning for Data Injection Pollution
## 1 Introduction
In the massive data-driven machine learning method, the data pollution problem inevitably affects the robustness of the model, leading to learning shock and an insufficient prediction ability. However, through establishing a complete data cleaning process, adopting an anomaly detection algorithm, and establishing a data-quality evaluation system, the model can improve its tolerance of data pollution faults, thus improving its robustness. Under the federated learning framework, there are two main sources of data pollution:  
(1) Incontamination of raw data: This kind of pollution is relatively common. For classification and regression models, the cause of data pollution may be the change in sample distribution or disordered sample labeling. When the original data are contaminated, the detection and filtering of contaminated samples become the focus of research. Whether abnormal samples can be detected quickly and effectively is crucial to the training of machine learning models. (2) Contamination generated during data transmission: In the federal learning framework, there is frequent data transfer between global trainers and the local trainers, which can easily produce pollution in the process of data transmission. When the trainer uploads gradient pollution, the upload gradient security aggregation from the global trainer to the local trainers is prone to improper aggregation and algorithm training shock, and may even fail to converge. In this case, the parameters are broadcast to the local trainers, leading to the pollution of the model parameters under the federal framework. In conclusion, this article studies an important AFL issue:how to detect gradient anomalies in a data pollution environment--and proposes an improved algorithm named Asynchronous Federated Learning Improving (AFLI), which focuses on the impact of data injection contamination on gradient information. The main work and contributions of this article are as follows:  
(1) When the original data are contaminated, it is difficult for the local trainer to accurately identify and filter the data, which transfers the parameter gradient of the abnormal data to the global trainer and then causes the systematic parameter pollution problem. In this article, we preprocess existing datasets to improve the ability of the algorithm to identify noise data. (2) An anomaly detection algorithm is used during the gradient transmission process. There is frequent transmission of parameters between the local trainer and the global trainer. When the local trainer transfers its own gradient to the global trainer, severe gradient contamination will cause the global trainer to effectively update the parameters. In order to solve this problem, this article mainly adopts two methods: first, the gradient is uploaded by each local trainer through the experience pool technology; second, the ITF (Isolation Forest) algorithm is used to detect and eliminate the abnormal gradient, and only the normal gradients are used to update the parameters of the global trainer to effectively alleviate the influence of the abnormal gradient on the parameter update. (3) Based on previous work, this article achieved relatively high accuracy in two data sets, including two CLS and ZXSFL. Various experiments demonstrate that the algorithm can effectively detect contaminated data sets and achieve high accuracy.