CN112364943A - Federal prediction method based on federal learning - Google Patents

Federal prediction method based on federal learning Download PDF

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CN112364943A
CN112364943A CN202011456395.8A CN202011456395A CN112364943A CN 112364943 A CN112364943 A CN 112364943A CN 202011456395 A CN202011456395 A CN 202011456395A CN 112364943 A CN112364943 A CN 112364943A
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李先贤
段锦欢
王金艳
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Abstract

The invention discloses a federal prediction method based on federal learning, which enables the parameter change of a neural network model updated by a single participant to have direction difference and no size difference by unitizing a locally updated gradient vector, thereby protecting the data privacy, simultaneously not using homomorphic, differential privacy or other encryption technologies, and greatly reducing the communication cost between equipment and a server under the condition of not losing the data precision. In addition, considering that the data are often large in difference in a federal learning scene, the performance of local participants can be improved by increasing the local information of the data, the uploaded neural network model parameters are clustered by using a k-means algorithm to obtain similar neural network model parameters, and the aggregation weight of the neural network model parameters is improved, so that the method is more suitable for the data scene of the participants.

Description

Federal prediction method based on federal learning
Technical Field
The invention relates to the technical field of federal learning, in particular to a federal prediction method based on federal learning.
Background
In most industries, due to problems of industry competition, privacy security, complex administrative procedures and the like, data is not shared, and even among different departments of the same company, centralized integration of data faces an important resistance. In reality, it is almost impossible to integrate data distributed in various places and organizations, and the cost is enormous. Meanwhile, all countries strengthen the protection of data security and privacy, and a new law General Data Protection Regulation (GDPR) introduced recently in the European Union shows that the strictness of the user data privacy and security management is a world trend. Aiming at two problems of data island and privacy safety, Google proposed a federal learning framework in 2016, and the design goal of the Federa learning framework is to develop efficient machine learning among multiple participants or multiple computing nodes on the premise of guaranteeing information safety during big data exchange, protecting terminal data and personal data privacy and guaranteeing legal compliance.
The federal learning is that a plurality of data owners form a alliance and participate in the training of the global model together. On the basis of protecting data privacy and model parameters, all the participants only share the encrypted model parameters or the encrypted intermediate calculation results, but do not share original data, so that the data can be used and invisible, and the jointly constructed model can achieve better model performance. With the continuous perfection of laws and regulations in data security, more and more companies and organizations schedule privacy security, and more researchers invest the data security.
In federal learning, using matrix DiRepresenting the data held by each data owner i, each row of the matrix representing a sample, and each column representing a feature. Meanwhile, some data sets may also contain label data, for example, a label in the financial field may be a credit of a user, a label in the marketing field may be a purchasing desire of the user, and a label in the education field may be a degree of a student. For this purpose, the feature space is denoted by X, the label space by Y and the sample ID space by I, so that a complete training data set (I, X, Y) is formed among the feature X, the label Y and the sample ID. In existing federal learning, its parameters are in the polymerization processA pure federal averaging mode is adopted, and the prediction performance of the model obtained by the mode is poor. In addition, because encryption is required on the shared parameters, the communication cost between the equipment and the server can be greatly increased by using homomorphic or other encryption technologies, and the communication efficiency is low.
Disclosure of Invention
The invention aims to solve the problem of poor prediction effect of the existing federal learning and provides a federal prediction method based on the federal learning.
In order to solve the problems, the invention is realized by the following technical scheme:
a federal forecast method based on federal learning comprises the following steps:
step 1, a server initializes parameters of a neural network model and sends the parameters to all participants; all participants take the parameters of the initialized neural network model as model parameters of the 0 th round of the local neural network model;
step 2, the participation direction server meeting the conditions provides a request for participating in the t-th round of training, and the server selects cK participants as training participants to participate in the t-th round of training;
step 3, each training participant of the t round of training utilizes a local training data set DkTraining a local neural network model, and updating model parameters of the t-1 th round of the local neural network model by using a random gradient descent method and gradient unit vectors in the training process
Figure BDA0002828727710000021
Obtaining the t round uploading model parameter of the local neural network model
Figure BDA0002828727710000022
Step 4, uploading model parameters of the t-th round of the local neural network model by all training participants of the t-th round of training
Figure BDA0002828727710000023
Uploading to a server;
step 5, uploading model parameters to the tth round by the server based on a k-means clustering algorithm
Figure BDA0002828727710000024
Polymerizing to obtain the final model parameter of the discrete cluster of the t-th round
Figure BDA0002828727710000025
And the t round clustering final model parameter of each clustering cluster
Figure BDA0002828727710000026
Step 6, the server enables the t-th clustering cluster final model parameter of each clustering cluster
Figure BDA0002828727710000027
Respectively sending the parameters to training participants corresponding to the t-th round clustering point model parameters of the corresponding clustering clusters, and obtaining the final model parameters of the t-th round discrete clusters of the discrete clusters
Figure BDA0002828727710000028
Transmitting the training participator corresponding to the discrete point model parameter of the t-th round of the discrete cluster and other participators which do not participate in the t-th round;
step 7, judging whether the local neural network models of all the participants have converged or reach the preset training times: if yes, go to step 8; otherwise, making t equal to t +1, and returning to the step 2;
and 8, each participant sends the local test data into the local neural network model, and the local test data is predicted by using the local neural network model.
The specific process of the step 3 is as follows:
step 3.1, training participant k utilizes local training data set DkTraining the current local neural network model, and updating model parameters of the t-1 th round of the local neural network model by using a random gradient descent method and gradient unit vectors in the training process
Figure BDA0002828727710000029
Obtaining the first model parameter of the t-th round of the local neural network model
Figure BDA00028287277100000210
Step 3.2, training participant k first from local training data set DkSelecting a part of data to form a part of local training data set BkReuse part of the local training data set BkTraining the current local neural network model, and updating the first model parameter of the t-th round of the local neural network model by using a random gradient descent method and gradient unit vectors in the training process
Figure BDA00028287277100000211
Obtaining the second model parameter of the t-th round of the local neural network model
Figure BDA00028287277100000212
Step 3.3, training participant k utilizes local training dataset DkTraining the current local neural network model, and updating the second model parameter of the t-th round of the local neural network model by using a random gradient descent method and gradient unit vectors in the training process
Figure BDA00028287277100000213
Obtaining the t round uploading model parameter of the local neural network model
Figure BDA00028287277100000214
The specific process of the step 5 is as follows:
step 5.1, the server uploads all the t-th round uploading model parameters by using a k-means clustering algorithm
Figure BDA0002828727710000031
Clustering into M clustering clusters, and calculating the cluster center coordinate of each clustering cluster;
step 5.2, the server follows eachClustering cluster t round uploading model parameter
Figure BDA0002828727710000032
Selecting a t-th round uploading model parameter of which the distance between the parameter and the cluster center coordinate of the cluster is out of a preset range
Figure BDA0002828727710000033
As the t-th discrete point model parameters, forming a new discrete cluster by the t-th discrete point model parameters selected from all the cluster clusters; at the moment, uploading model parameters of the remaining t-th round of each cluster
Figure BDA0002828727710000034
As the t-th round clustering point model parameter;
step 5.3, the server selects a certain number of discrete point model parameters of the t-th round from all discrete point model parameters of the t-th round of the discrete clusters to average, and the discrete cluster intermediate model parameters of the t-th round of the discrete clusters are obtained
Figure BDA0002828727710000035
Meanwhile, the server averages the t-th round clustering point model parameters in all the t-th round clustering point model parameters of each clustering cluster to obtain the t-th round clustering cluster middle model parameters of each clustering cluster
Figure BDA0002828727710000036
Step 5.4, the server calculates the t-th round discrete cluster final model parameter of the discrete cluster
Figure BDA0002828727710000037
And the t round clustering final model parameter of each clustering cluster
Figure BDA0002828727710000038
Wherein:
Figure BDA0002828727710000039
Figure BDA00028287277100000310
the alpha is the weight of the current cluster i, beta is the weight of other clusters j, gamma is the weight of a discrete cluster, alpha + beta + gamma is 1, and alpha > beta > gamma;
Figure BDA00028287277100000311
the Euclidean distance between the cluster center coordinate of the current cluster i and the cluster center coordinates of other cluster j is obtained; i belongs to M, j is not equal to i, and M is the number of the clustering clusters; k belongs to cK, and the cK is the number of the training participants; t is the number of training rounds.
Compared with the prior art, the invention has the following characteristics:
1. considering that the data are often large in difference in a federal learning scene, the performance of the local participants can be improved by increasing the local information of the data, the method utilizes a k-means algorithm to cluster uploaded neural network model parameters to obtain similar neural network model parameters, improves the aggregation weight of the neural network model parameters, and is more suitable for the data scene of the local participants.
2. According to the method, the gradient vector unit of local update is adopted, so that the change of the neural network model parameters updated by a single participant is only different in direction and has no difference in size, the data privacy is protected, homomorphic privacy, differential privacy or other encryption technologies are not needed, and the communication cost between equipment and a server is greatly reduced under the condition of not losing the data precision.
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FIG. 1 is a schematic diagram of a federated prediction method based on federated learning.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to specific examples.
For those skilled in the art of federal learning, before federal training can be performed, it is necessary to identify federal learning participants and servers and build a federal learning environment. Before participating in federal learning, a participant needs to prepare a local training data set to be trained, and needs a local neural network model determined according to an actual application scenario.
Taking keyboard input prediction as an example, the participating parties are thousands of mobile devices (mobile phones) participating in federal learning, and the server is Ariiyun or Baidu cloud. The local training data set is the data of input method (e.g., Gboard) users who select a shared text segment when entering the google application. The text is truncated to contain a phrase consisting of several words, and segments are only occasionally logged in from individual users. Prior to training, the logs were anonymized and deprived of personally identifiable information. Furthermore, segments are only used for training when starting with a sentence mark. The local neural network model uses a variant of the long-short term memory (LSTM) recurrent neural network, called the coupled-input forgetting gate (CIFG), for the next-word prediction model, and bound input-embedded and output projection matrices are used to reduce function size and accelerate training. Given a vocabulary of size V, by d Wv and an embedding matrix w ∈ RD×VEncoding a single hotspot v epsilon RVMapping to a dense embedded vector d ∈ RD. Output projection of CIFG, also at RDIs mapped to WTh∈RVThe output vector of (1). The softmax function on the output vector converts the raw logarithm to a normalized probability, and the model is trained using the cross entropy loss of the output label and the target label. Virtual keyboard input suggests that users all use mobile devices (cell phones), but the data input by different users on the keyboard is different.
Taking bank money laundering prediction as an example, the participating party is a bank participating in federal study, and the server is Ali cloud or Baidu cloud. The local training data set is business data of a bank, and the data comprises four items of client id, number x1 of inconsistent fund source and operation range, number x2 of large transaction and whether label data Y is used for money laundering. The local neural network model is set as a fully-connected neural network with two layers of three nodes and a softmax function, and the activation function is relu. The bank anti-money laundering task, bank A and bank B are in different areas. The business data of the bank A and the bank B have the same characteristic space and different customers due to the same business.
Referring to fig. 1, a federal prediction method based on federal learning includes the following steps:
step 1, a server initializes parameters of a neural network model and sends the parameters to all K participants; all participants take the parameters of the initialized neural network model as the model parameters of the 0 th round of the local neural network model
Figure BDA0002828727710000041
Wherein K belongs to K, and K is the number of the participants.
In the keyboard input prediction example, the 0 th round raw model parameters are broadcast to all mobile devices (handsets) participating in federal training.
In the bank money laundering prediction example, round 0 original model parameters are broadcast to all bank devices participating in federal training.
And 2, the participation direction meeting the conditions provides a request for participating in the t-th round of training to the server, and the server selects cK participants as training participants to participate in the t-th round of training.
Participants meeting a certain condition (the condition is determined according to the task of the federal learning training plan) can make a request to the server, and the server can participate in the current training, after receiving the request, a part of the participants can be selected to participate in the current training, if some participants do not participate in the current training, the server can make the requests again after a period of time, and the server can consider the factors of the number of the participants and the overtime time. This round of training will only succeed if enough devices can participate in the current round of training before a timeout.
Step 3, each training participant of the t round of training utilizes a local training data set DkTraining a local neural network model, and updating model parameters of the t-1 th round of the local neural network model by using a random gradient descent method and gradient unit vectors in the training process
Figure BDA0002828727710000051
Obtaining the t round uploading model parameter of the local neural network model
Figure BDA0002828727710000052
Wherein k belongs to cK, and cK is the number of training participants.
By unitizing the locally updated gradient vectors, the parameter change of the neural network model updated by a single participant only has direction difference and no size difference. Even if an attacker obtains the neural network model parameters, only the gradient unit vector is known, and the local data cannot be reversely pushed out, so that the data privacy is protected. When uploading the parameters, the parameters do not need to be encrypted, and the communication efficiency between the participants and the server is improved.
Step 3.1, training participant k utilizes local training data set DkTraining the current local neural network model, and updating model parameters of the t-1 th round of the local neural network model by using a random gradient descent method and gradient unit vectors in the training process
Figure BDA0002828727710000053
Obtaining the first model parameter of the t-th round of the local neural network model
Figure BDA0002828727710000054
This phase is trained once.
Figure BDA0002828727710000055
In the formula, eta is the learning rate,
Figure BDA0002828727710000056
for the t-th round of training, the model parameter gradient of sample x in party k.
Step 3.2, training participant k first from local training data set DkSelecting a part forming part of the local training data set BkReuse of part of the local training dataSet BkTraining the current local neural network model, and updating the first model parameter of the t-th round of the local neural network model by using a random gradient descent method and gradient unit vectors in the training process
Figure BDA0002828727710000057
Obtaining the second model parameter of the t-th round of the local neural network model
Figure BDA0002828727710000058
This phase may be trained multiple times.
Figure BDA0002828727710000059
In the formula, eta is the learning rate,
Figure BDA00028287277100000510
for the t-th round of training, the model parameter gradient of sample x in party k.
Step 3.3, training participant k utilizes local training dataset DkTraining the current local neural network model, and updating the second model parameter of the t-th round of the local neural network model by using a random gradient descent method and gradient unit vectors in the training process
Figure BDA00028287277100000511
Obtaining the t round uploading model parameter of the local neural network model
Figure BDA00028287277100000512
This phase is trained once.
Figure BDA00028287277100000513
In the formula, eta is the learning rate,
Figure BDA00028287277100000514
for the sample in the t round training, participant kThe model parameter gradient of this x.
In the keyboard input prediction example, the mobile devices participating in the current round of training input a local training data set (input method user's data) including log data and log text into the local neural network model.
In the bank money laundering prediction example, the bank equipment participating in the current round of training inputs a local training data set (business data of the bank) into a local neural network model, wherein the local training data set comprises four items including client id, the number x1 of the capital source and the operating range which do not accord with each other, the number x2 of the large transaction number, and whether the label data Y is money laundering or not.
Step 4, uploading model parameters of the t-th round of the local neural network model by all training participants of the t-th round of training
Figure BDA0002828727710000061
And uploading to a server.
And the server waits for each participant to return the result after training, and if enough participants return the result before timeout, the training of the round is successful, otherwise, the training of the round fails. The server adopts an aggregation algorithm to aggregate after the training of the round is successful.
Step 5, uploading model parameters to the tth round by the server based on a k-means clustering algorithm
Figure BDA0002828727710000062
Polymerizing to obtain the final model parameter of the t round cluster
Figure BDA0002828727710000063
And the t-th discrete cluster final model parameters
Figure BDA0002828727710000064
Due to the fact that the parameters are directly averaged by the federal average, the data are often large in difference under the federal learning scene, and the performance of local participants can be improved by increasing the local information of the data. And clustering the uploaded neural network model parameters by using a k-means algorithm to obtain similar neural network model parameters. During aggregation, the weight of the neural network model parameters of the local cluster is improved for local data, so that the method is more suitable for the data scene of the participant and improves the performance of the neural network model.
Step 5.1, the server uploads all the t-th round uploading model parameters by using a k-means clustering algorithm
Figure BDA0002828727710000065
Clustering into M cluster clusters, and calculating cluster center coordinates of each cluster
Figure BDA0002828727710000066
Wherein i belongs to M, and M is the number of the clustering clusters.
Step 5.2, uploading model parameters from the t-th round of each cluster by the server
Figure BDA0002828727710000067
Selecting a t-th round uploading model parameter of which the distance between the parameter and the cluster center coordinate of the cluster is out of a preset range
Figure BDA0002828727710000068
As the t-th discrete point model parameters, forming a new discrete cluster by the t-th discrete point model parameters selected from all the cluster clusters; at the moment, uploading model parameters of the remaining t-th round of each cluster
Figure BDA0002828727710000069
As the t-th round clustering point model parameters.
Uploading model parameters when the tth round
Figure BDA00028287277100000610
Coordinates of the cluster center
Figure BDA00028287277100000611
The Euclidean distance between them is less than or equal to the preset range, i.e.
Figure BDA00028287277100000612
Then the t round uploads the model parameters
Figure BDA00028287277100000613
Called as the t-th round clustering point model parameter (the training participant corresponding to the parameter is the normal training participant in the cluster). Uploading model parameters when the tth round
Figure BDA00028287277100000614
Coordinates of the cluster center
Figure BDA00028287277100000615
Having a Euclidean distance between them greater than a predetermined range, i.e.
Figure BDA00028287277100000616
Then the t round uploads the model parameters
Figure BDA00028287277100000617
And the parameter is called as the t-th round discrete point model parameter (the training participant corresponding to the parameter is the abnormal training participant in the cluster). Wherein s isiThe standard deviation of all model parameters in the i cluster to cluster center coordinates.
Step 5.3, the server selects Q discrete point model parameters of the t round from all discrete point model parameters of the t round of the discrete clusters to average to obtain discrete cluster intermediate model parameters of the t round of the discrete clusters
Figure BDA00028287277100000618
Meanwhile, the server selects a certain number of Q cluster parameters from all the t round clustering point model parameters of each cluster to be averaged to obtain the t round clustering cluster intermediate model parameters of each cluster
Figure BDA00028287277100000619
Figure BDA0002828727710000071
Figure BDA0002828727710000072
Step 5.4, the server calculates the t-th round discrete cluster final model parameter of the discrete cluster
Figure BDA0002828727710000073
And the t round clustering final model parameter of each clustering cluster
Figure BDA0002828727710000074
Wherein:
Figure BDA0002828727710000075
Figure BDA0002828727710000076
where α is the weight of the current cluster i, β is the weight of the other cluster j, and γ is the weight of the discrete cluster, and α + β + γ is 1 and α > β > γ. Wherein
Figure BDA0002828727710000077
And in the t-th round of training, the Euclidean distance between the current cluster i and the cluster center coordinates of other cluster j is calculated.
Step 6, the server enables the t-th clustering cluster final model parameter of each clustering cluster
Figure BDA0002828727710000078
Respectively sending the parameters to training participators (normal training participators) corresponding to the t-th round clustering point model parameters of the corresponding clustering clusters, and finally obtaining model parameters of the t-th round discrete clusters of the discrete clusters
Figure BDA0002828727710000079
And transmitting the parameters to training participants (abnormal training participants) corresponding to the discrete point model parameters of the t-th round of the discrete clusters and other participants not participating in the t-th round.
Step 7, judging whether the local neural network models of all the participants have converged or reach the preset training times: if yes, go to step 8; otherwise, let t be t +1, and return to step 2.
The model convergence refers to that the loss function value of the local neural network model is not changed any more or the change amount is smaller than a set change threshold.
And 8, each participant sends the local test data into the local neural network model, and the local test data is predicted by using the local neural network model.
In the keyboard input prediction example, each mobile device obtains a trained coupled input forgetting gate neural network model, the mobile device takes the real-time keyboard input characters of a user as input values of the coupled input forgetting gate neural network model, the output values of the coupled input forgetting gate neural network model are the next predicted input characters, and the predicted input characters are displayed on a virtual keyboard for the user to select.
In the bank money laundering prediction example, each bank obtains a trained anti-money laundering neural network model, bank business data comprising three items of client id, number x1 of inconsistent fund source and operation range and number x2 of large-amount transaction are used as input values of the anti-money laundering neural network model, and the output value of the anti-money laundering neural network model is used for predicting whether the business data is suspected to participate in money laundering.
It should be noted that, although the above-mentioned embodiments of the present invention are illustrative, the present invention is not limited thereto, and thus the present invention is not limited to the above-mentioned embodiments. Other embodiments, which can be made by those skilled in the art in light of the teachings of the present invention, are considered to be within the scope of the present invention without departing from its principles.

Claims (5)

1.一种基于联邦学习的联邦预测方法，其特征是，包括步骤如下：1. A federated prediction method based on federated learning, characterized in that it comprises the following steps: 步骤1、服务器初始化神经网络模型的参数，并发送给所有的参与方；所有参与方将初始化神经网络模型的参数作为本地神经网络模型的第0轮模型参数；Step 1. The server initializes the parameters of the neural network model and sends it to all the participants; all the participants take the parameters of the initialized neural network model as the 0th round model parameters of the local neural network model; 步骤2、满足条件的参与方向服务器提出参与第t轮训练的请求，服务器从中选择cK个参与方作为训练参与方参与到第t轮训练中；Step 2. Participants who meet the conditions submit a request to the server to participate in the t-th round of training, and the server selects cK participants from them to participate in the t-th round of training as training participants; 步骤3、第t轮训练的每个训练参与方利用本地训练数据集Dk对本地神经网络模型进行训练，在训练过程中运用随机梯度下降法并通过梯度单位向量去更新本地神经网络模型的第t-1轮模型参数
Figure FDA0002828727700000011
得到本地神经网络模型的第t轮上传模型参数
Figure FDA0002828727700000012
Step 3. Each training participant in the t-th round of training uses the local training data set Dk to train the local neural network model, and uses the stochastic gradient descent method during the training process to update the local neural network model through the gradient unit vector. t-1 round model parameters
Figure FDA0002828727700000011
Get the t-th upload model parameters of the local neural network model
Figure FDA0002828727700000012
 步骤4、第t轮训练的所有训练参与方将其本地神经网络模型的第t轮上传模型参数
Figure FDA0002828727700000013
上传给服务器；Step 4. All training participants in the t-th round of training upload the model parameters for the t-th round of their local neural network model
Figure FDA0002828727700000013
upload to the server; 步骤5、服务器基于k-means聚类算法对第t轮上传模型参数
Figure FDA0002828727700000014
进行聚合，得到离散簇的第t轮离散簇最终模型参数
Figure FDA0002828727700000015
和每个聚类簇的第t轮聚类簇最终模型参数
Figure FDA0002828727700000016
Step 5. The server uploads the model parameters for the t round based on the k-means clustering algorithm
Figure FDA0002828727700000014
Aggregate to get the final model parameters of the t-th round of discrete clusters of discrete clusters
Figure FDA0002828727700000015
and the final model parameters of the t-th round of clusters for each cluster
Figure FDA0002828727700000016
 步骤6、服务器将每个聚类簇的第t轮聚类簇最终模型参数
Figure FDA0002828727700000017
分别发送给相应聚类簇的第t轮聚类点模型参数所对应的训练参与方，并将离散簇的第t轮离散簇最终模型参数
Figure FDA0002828727700000018
发送给离散簇的第t轮离散点模型参数所对应的训练参与方和未参与第t轮的其他参与方；Step 6. The server sets the final model parameters of each cluster in the t-th round of clustering
Figure FDA0002828727700000017
They are respectively sent to the training participants corresponding to the model parameters of the t-th round of clustering points of the corresponding cluster clusters, and the final model parameters of the t-th round of discrete clusters of discrete clusters are sent to the training participants.
Figure FDA0002828727700000018
Send to the training participants corresponding to the discrete point model parameters of the t-th round of discrete clusters and other participants who did not participate in the t-th round; 步骤7、判断所有参与方的本地神经网络模型是否已经收敛或达到预定的训练次数：如果是，则转至步骤8；否则，令训练轮数t加1，并返回步骤2；Step 7. Determine whether the local neural network models of all participants have converged or have reached a predetermined number of training times: if so, go to step 8; otherwise, add 1 to the number of training rounds t, and return to step 2; 步骤8、各参与方将本地测试数据送入到本地神经网络模型中，利用本地神经网络模型完成对本地测试数据的预测；Step 8, each participant sends the local test data into the local neural network model, and uses the local neural network model to complete the prediction of the local test data; 上述k∈cK，cK为训练参与方的个数；t为训练轮数。The above k∈cK, cK is the number of training participants; t is the number of training rounds. 2.根据权利要求1所述的一种基于联邦学习的联邦预测方法，其特征是，步骤3的具体过程如下：2. A federated prediction method based on federated learning according to claim 1, wherein the specific process of step 3 is as follows: 步骤3.1、训练参与方k利用本地训练数据集Dk对当前本地神经网络模型进行训练，在训练过程中运用随机梯度下降法并通过梯度单位向量去更新本地神经网络模型的第t-1轮模型参数
Figure FDA0002828727700000019
得到本地神经网络模型的第t轮第一模型参数
Figure FDA00028287277000000110
Step 3.1. The training participant k uses the local training data set D k to train the current local neural network model. During the training process, the stochastic gradient descent method is used to update the t-1 round model of the local neural network model through the gradient unit vector. parameter
Figure FDA0002828727700000019
Obtain the first model parameters of the t-th round of the local neural network model
Figure FDA00028287277000000110
 步骤3.2、训练参与方k先从本地训练数据集Dk选出一部分数据形成部分本地训练数据集Bk，再利用部分本地训练数据集Bk对当前本地神经网络模型进行训练，在训练过程中运用随机梯度下降法并通过梯度单位向量去更新本地神经网络模型的第t轮第一模型参数
Figure FDA00028287277000000111
得到本地神经网络模型的第t轮第二模型参数
Figure FDA0002828727700000021
Step 3.2. The training participant k first selects a part of the data from the local training data set D k to form a part of the local training data set B k , and then uses the part of the local training data set B k to train the current local neural network model. During the training process Use the stochastic gradient descent method to update the first model parameters of the t-th round of the local neural network model through the gradient unit vector
Figure FDA00028287277000000111
Obtain the second model parameters of the t-th round of the local neural network model
Figure FDA0002828727700000021
 步骤3.3、训练参与方k利用本地训练数据集Dk对当前本地神经网络模型进行训练，在训练过程中运用随机梯度下降法并通过梯度单位向量去更新本地神经网络模型的第t轮第二模型参数
Figure FDA0002828727700000022
得到本地神经网络模型的第t轮上传模型参数
Figure FDA0002828727700000023
Step 3.3. The training participant k uses the local training data set D k to train the current local neural network model. During the training process, the stochastic gradient descent method is used to update the second model of the t-th round of the local neural network model through the gradient unit vector. parameter
Figure FDA0002828727700000022
Get the t-th upload model parameters of the local neural network model
Figure FDA0002828727700000023
 上述k∈cK，cK为训练参与方的个数；t为训练轮数。The above k∈cK, cK is the number of training participants; t is the number of training rounds. 3.根据权利要求2所述的一种基于联邦学习的联邦预测方法，其特征是，3. A federated prediction method based on federated learning according to claim 2, wherein, 步骤3.1中，利用本地训练数据集Dk对本地神经网络模型进行一次训练；步骤3.2中，利用本地训练数据集Bk对本地神经网络模型进行多次训练；步骤3.3中，利用本地训练数据集Dk对本地神经网络模型进行一次训练；In step 3.1, use the local training data set D k to train the local neural network model once; in step 3.2, use the local training data set B k to train the local neural network model multiple times; in step 3.3, use the local training data set Dk trains the local neural network model once; 上述k∈cK，cK为训练参与方的个数。The above k∈cK, cK is the number of training participants. 4.根据权利要求1所述的一种基于联邦学习的联邦预测方法，其特征是，步骤5的具体过程如下：4. A federated prediction method based on federated learning according to claim 1, wherein the specific process of step 5 is as follows: 步骤5.1、服务器利用k-means聚类算法将所有的第t轮上传模型参数
Figure FDA0002828727700000024
聚类为M个聚类簇，并计算每个聚类簇的簇心坐标；Step 5.1. The server uses the k-means clustering algorithm to upload all model parameters for the t-th round
Figure FDA0002828727700000024
Cluster into M clusters, and calculate the cluster center coordinates of each cluster; 步骤5.2、服务器从每个聚类簇的第t轮上传模型参数
Figure FDA0002828727700000025
中挑选出与其聚类簇的簇心坐标之间的距离在预设范围之外的第t轮上传模型参数
Figure FDA0002828727700000026
作为第t轮离散点模型参数，并将所有聚类簇所选出的第t轮离散点模型参数形成一个新的离散簇；此时，每个聚类簇剩下的第t轮上传模型参数
Figure FDA0002828727700000027
作为第t轮聚类点模型参数；Step 5.2, the server uploads model parameters from the t-th round of each cluster
Figure FDA0002828727700000025
In the t-th round, upload the model parameters with the distance between the cluster center coordinates of the cluster and its cluster outside the preset range.
Figure FDA0002828727700000026
As the t-th round of discrete point model parameters, the t-th round of discrete-point model parameters selected from all cluster clusters are formed into a new discrete cluster; at this time, the remaining t-th round of each cluster cluster uploads the model parameters
Figure FDA0002828727700000027
as the t-th round of clustering point model parameters; 步骤5.3、服务器从离散簇的所有第t轮离散点模型参数中选出一定数量的第t轮离散点模型参数进行平均，得到离散簇的第t轮离散簇中间模型参数
Figure FDA0002828727700000028
同时，服务器从每个聚类簇的所有第t轮聚类点模型参数中第t轮聚类点模型参数进行平均，得到每个聚类簇的第t轮聚类簇中间模型参数
Figure FDA0002828727700000029
Step 5.3. The server selects a certain number of t-th round of discrete point model parameters from all the t-th round of discrete point model parameters of the discrete cluster and averages them to obtain the t-th round of discrete cluster intermediate model parameters of the discrete cluster
Figure FDA0002828727700000028
At the same time, the server averages the t-th round of cluster point model parameters from all the t-th round of cluster point model parameters of each cluster to obtain the t-th round of cluster intermediate model parameters of each cluster.
Figure FDA0002828727700000029
 步骤5.4、服务器计算离散簇的第t轮离散簇最终模型参数
Figure FDA00028287277000000210
和每个聚类簇的第t轮聚类簇最终模型参数
Figure FDA00028287277000000211
其中：Step 5.4, the server calculates the final model parameters of the discrete cluster in the t-th round of discrete clusters
Figure FDA00028287277000000210
and the final model parameters of the t-th round of clusters for each cluster
Figure FDA00028287277000000211
in:
Figure FDA00028287277000000212
Figure FDA00028287277000000212
Figure FDA00028287277000000213
Figure FDA00028287277000000213
 上述α为当前聚类簇i的权重，β为其他聚类簇j的权重，γ为离散簇的权重，α+β+γ＝1且α＞β＞γ；
Figure FDA00028287277000000214
为当前聚类簇i的簇心坐标与其他聚类簇j的簇心坐标的欧式距离；i∈M，j∈M，j≠i，M为聚类簇的个数；k∈cK，cK为训练参与方的个数；t为训练轮数。The above α is the weight of the current cluster i, β is the weight of other clusters j, γ is the weight of the discrete cluster, α+β+γ=1 and α>β>γ;
Figure FDA00028287277000000214
is the Euclidean distance between the cluster center coordinates of the current cluster i and the cluster center coordinates of other clusters j; i∈M, j∈M, j≠i, M is the number of clusters; k∈cK, cK is the number of training participants; t is the number of training rounds. 5.根据权利要求1所述的一种基于联邦学习的联邦预测方法，其特征是，步骤7中，参与方的本地神经网络模型收敛是指本地神经网络模型的损失函数值不再变化或变化量小于设定的变化阈值。5. A federated prediction method based on federated learning according to claim 1, wherein in step 7, the convergence of the local neural network model of the participant means that the loss function value of the local neural network model no longer changes or changes. The amount is less than the set change threshold.
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