## 1 Introduction  
With the development of artificial intelligence, especially the emergence of neural networks, artificial intelligence technology driven by big data has played its role in many complex fields, such as automatic driving, medical care, and finance. People want this technology to be realized in all walls of life. However, the actual situation is very disappointing. Except for a few limited fields, many industries have limited data and poor data quality, which is insufficient to support the implementation of artificial intelligence technology [1]. Even if there are many data in more application fields, these data are distributed among different data sources and marked unevenly. For example, there are 537,100 schools of all levels and types in China, with 289 million students. These students' data are storedin different schools and on various online education platforms. These data sources are not connected to each other, and the data exist in the form of isolated islands. It is almost impossible to integrate these data, or the cost required is enormous. On the other hand, with the further development of big data, data privacy and security have become a worldwide trend. Because of IoT devices' increasing storage and computing capabilities, it is common to save data and models on local devices [2]. Federated learning is a new frontier field [1; 3; 4]. Google has launched a federated learning system [3; 4; 5], in which a federated of distributed participants updates the global machine learning model and their data are stored locally. Their framework requires all contributors to share the same feature space. On the other hand, secure machine learning for partitioning data in feature space is also studied [6; 7; 8], which finds that, in machine learning, each participant can make joint modeling with the help of other parties' data. However, all parties do not need to share data resources. They can conduct federated data training and establish a shared machine learning model when the data do not come out locally. Federated learning enables multiple participants to continue machine learning and deep learning in protecting data privacy and meeting legal compliance requirements to solve the data island problem. Federated learning technology can be used among mobile terminals, wearable devices, and different platforms to realize a training machine learning model that pays more attention to privacy and efficiency. However, when one party collects a lot of unlabeled data, even when the features are statistically different from those collected by the other party, domain shift will arise [9]. This makes the model obtained by federated learning and training unable to be extended to new equipment. For example, in the field of education, two different online education platforms save local student data. One platform may collect more student behavior logs, while the other platform saves students' personal information. In the field of educational data mining, the classification of students' grades is usually limited by the size of datasets and the number of data features. Moreover, because of data privacy protection, each data platform cannot carry out practical federated training. Therefore, it is an urgent problem to solve the imbalance of data and the inconsistency of characteristics among data owners to train student models. This paper proposes a framework based on federated transfer learning for students' grades classification with privacy protection. In this paper, heterogeneous domain adaptive technology is introduced into federated transfer learning, and knowledge transfer between different nodes is solved by exchanging encryption parameters between data. In this framework, each data owner uses the network of label classifier and domain classifier for training and aligns the features of all parties in the new feature space for transfer learning.