## 2 Related Work
### Federated Learning  
Google recently proposed the concept of federated learning [3; 4; 5]. Their main idea is to build a machine learning model based on datasets distributed on multiple devices while preventing data leakage. Recent improvements focus on overcoming statistical challenges [2; 33] and improving the security of federated learning [34; 35], and there are some research efforts to make federated learning more personalized [36]. All of the above work focuses on device federated learning, involving distributed mobile user interaction and communication cost, unbalanced data distribution, and device reliability in large-scale distribution are some of the main factors of optimization. To extend the concept of federated learning to cover collaborative learning scenarios among organizations, [PERSON] et al. expanded the original federated learning into the general idea of all decentralized collaborative machine learning technologies for privacy protection [1]. They made a comprehensive overview of federated learning and federated transfer learning technologies. At the same time, they further investigated the related security foundation and discussed the relationship with other associated fields, such as multi-agent theory and privacy protection data mining. [PERSON] et al. proposed a new lossless privacy protection algorithm secure-boost, which is used to train high-quality tree enhancement models when the training data are kept secret on multiple parties [7]. [PERSON] et al. proposed a new distributed machine learning method, called splitted. Due to network fragmentation, it provides a higher level of privacy than federated learning. It is suitable for machine learning (ML) with low computational resources, fast training, and analysis of private and sensitive data [37]. [PERSON] et al. studied the feasibility of data and model poisoning attacks under the blockchain based FL system built next to the Ethereum network and the traditional FL system (no blockchain) [38]. They proposed a transparent incentive mechanism to fill the knowledge gap, which can encourage good behavior among participating decentralized nodes, avoid common problems, and provide knowledge for FL security literature by studying the current FL system. In addition, the time required for computing is also an important factor. Federated learning (FL) is largely affected by the heterogeneity of clients. The training speed of clients is inconsistent. In order to reduce the impact of slow clients, [PERSON] et al. proposed a new FL algorithm using model freezing and unloading, which uses a simple, fast, and scalable scheduling algorithm to periodically organize model unloading between clients [39]. To reduce the communication and computation delays of model parameters, [PERSON] et al. proposed a new iterative algorithm and minimized the total model parameter communication and computation delays by optimizing the number of local iterations and edge iterations [40]. Recent research has focused on transferring knowledge from decentralized nodes to new nodes with different data domains. [PERSON] et al. introduced new technology and framework called federated transfer learning (FTL) to improve the statistical model under the data alliance. The alliance allows knowledge sharing without compromising users' privacy and allows supplementary knowledge to be transmitted in the network [8]. Therefore, the target domain can build a more flexible and robust model using rich tags from the source domain. [PERSON] et al. put forward a principled method to solve the federated domain adaptation problem, which aims to make the representations learned between different nodes consistent with the data distribution of target nodes [41]. However, we do not think [PERSON]'s method extends to the constraint of federated learning. It cannot be applied to the general environment of federated learning.