## 2 Related Work
### Federated Learning  
Federated learning is also a kind of distributed learning, but differs from traditional distributed learning. The current distributed machine learning is still a kind of data centralized computing, but after the data is concentrated, the central server divides the sample subsets, and each sample subset trains the model and then aggregates the parameters of each model. Therefore, the samples in distributed machine learning are independent and identically distributed, and the sample size is uniformly distributed, so the training process of each subset is relatively similar. However, in federated learning, instead of concentrating data, algorithms are distributed to various devices to perform calculations. Compared with distributed learning, federated learning has more training subsets. In the mobile App start-up prediction scenario, a terminal user is equivalent to a training subset, and the training subsets located in different terminals may not be independent and identically distributed. In recent years, there have been many related studies on joint optimization, mainly for communication overhead and communication security. The current research reduces the communication overhead from two perspectives: reducing the communication rounds and reducing the dimensionality of communication data. Reference [20] uses the optimization method of the synchronous SGD algorithm [21] to improve the asynchronous stochastic gradient descent algorithm, so as to reduce the communication rounds between the client and the server, perform more calculations on the client, and quicken the model convergence. There are also studies using the same idea to improve the SVRG algorithm and apply it to federated learning [22]. In order to solve the problem of multiple communication rounds in federated learning based on the first-order stochastic gradient descent algorithm, [PERSON] et al. proposed a quasi-Newton method-based vertical federated learning framework for logistic regression under the additively homomorphic encryption scheme [23]. [PERSON] et al. proposed the Federated Stochastic Block Coordinate Descent (FedBCD) to effectively reduce the communication rounds for VFL [24]. The device and the server side need to interact with the complete model, and the large model needs to transmit more data. For the model update data uploaded on the device side, Low Rank, Random Mask, Subsampling, Probabilistic quantization, and other methods are used to reduce the dimensionality or compress the uplink data to reduce communication overhead [25]. According to the idea of federated learning, only the updated data of the model is used to interact between the client and the server, but the updated data still contains the characteristics of the client and may be reproduced. Some studies achieve privacy security in communication from the perspective of data encryption. For example, References [26; 27] adopted differential privacy methods to solve this problem. Reference [28] improved secure multi-party protocols for federated learning, to ensure that individual updates can be read only when enough users submit updates. In addition, the design of the framework and the system has been studied to realize the protection of communication data. [PERSON] et al. introduced a comprehensive secure the federated learning framework, covering three types of federated learning, building data networks among organizations based on federated mechanisms, and allowing knowledge to be shared without compromising user privacy [29]. [PERSON] et al. proposed a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning. The learning process of the system is executed by multiple parties with partially common user samples but different feature sets. It was theoretically proven that the system is as accurate as other non-privacy protection methods, and can also not disclose any user-related privacy data [30].