1. An enterprise financing trust method based on federal learning, which is characterized in that the method comprises the following steps:

receiving a financing request sent by an enterprise user, and acquiring corresponding bank dimension data according to the name of the enterprise user;

and determining a financing admission result of the enterprise user according to the bank dimension data and a federal learning model and returning the financing admission result to the enterprise user, wherein the federal learning model acquires corresponding gradient data and loss data from a third-party system through a homomorphic encryption rule in a model training process, and the gradient data and the loss data are acquired by the third-party system acquiring corresponding government affair dimension data through the homomorphic encryption rule and the name of the enterprise user so as to train the federal learning model.

2. The federal learning based enterprise financing crediting method as claimed in claim 1, further comprising:

and carrying out sample alignment operation on all the bank dimension data and the government dimension data of the enterprise user according to the asymmetric encryption rule and the name of the enterprise user to obtain the bank dimension data and the government dimension data for carrying out the federal learning model training.

3. The federal learning based enterprise financing crediting method as claimed in claim 1, further comprising:

establishing an initial federal learning model according to a preset federal learning random forest algorithm;

and acquiring and mutually transmitting corresponding gradient data and loss data through a homomorphic encryption rule in the initial federal learning model training convergence process to obtain the federal learning model with the model precision meeting a precision threshold.

4. The federal learning based enterprise financing crediting method as claimed in claim 1, further comprising:

acquiring newly added bank dimension data and government dimension data according to a set time period;

and acquiring and mutually transmitting corresponding gradient data and loss data to obtain an updated federal learning model.

5. The federal learning based enterprise financing crediting method as claimed in claim 1, wherein the determining of financing admission results for the enterprise users according to the bank dimension data and a federal learning model comprises:

and determining the financing admission result of the enterprise user according to the historical admission data, the historical financing data, the loan amount label and the federal learning model in the bank dimension data.

6. An enterprise financing and crediting device based on federal learning, comprising:

the bank dimension data determining module is used for receiving a financing request sent by an enterprise user and acquiring corresponding bank dimension data according to the name of the enterprise user;

and the federal learning model financing evaluation module is used for determining financing admission results of the enterprise users according to the bank dimension data and the federal learning model and returning the financing admission results to the enterprise users, wherein the federal learning model acquires corresponding gradient data and loss data from a third-party system through a homomorphic encryption rule in a model training process, and the gradient data and the loss data are acquired by the third-party system through the homomorphic encryption rule and the enterprise user name to acquire corresponding government service dimension data for federal learning model training.

7. The federal learning based enterprise financing crediting arrangement of claim 6, further comprising:

and the sample data alignment unit is used for performing sample alignment operation on all the bank dimension data and the government affair dimension data of the enterprise user according to the homomorphic encryption rule and the name of the enterprise user to obtain the bank dimension data and the government affair dimension data for performing the federal learning model training.

8. The federal learning based enterprise financing crediting arrangement of claim 6, further comprising:

the federal learning modeling unit is used for establishing an initial federal learning model according to a preset federal learning random forest algorithm;

and the federal learning training unit is used for acquiring and mutually transmitting corresponding gradient data and loss data through a homomorphic encryption rule in the initial federal learning model training convergence process to obtain the federal learning model with the model precision meeting the precision threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the federal learning based enterprise financing authorization method in any of claims 1 to 5.

10. A computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the federal learning based enterprise financing authorization method in any of claims 1 to 5.

Background

The mini-micro credit has the service characteristics of short term, small amount, high frequency and urgent demand, and the base number of the mini-micro customer group is huge. The weak risk resistance and the incomplete financial system are the visual characteristics of small and micro enterprises.

The inventor finds that the credit admission and risk prevention and control in the prior art are realized by making rules by business experts, and the manner of credit admission and risk prevention and control by only relying on the traditional 'expert loan curing' means is difficult to continue.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides the enterprise financing credit granting method and device based on the federal study, which can effectively integrate data of all parties on the premise of guaranteeing data privacy and improve the accuracy of enterprise financing credit granting.

In order to solve at least one of the above problems, the present application provides the following technical solutions:

in a first aspect, the present application provides a federal learning-based enterprise financing credit method, including:

receiving a financing request sent by an enterprise user, and acquiring corresponding bank dimension data according to the name of the enterprise user;

and determining a financing admission result of the enterprise user according to the bank dimension data and a federal learning model and returning the financing admission result to the enterprise user, wherein the federal learning model acquires corresponding gradient data and loss data from a third-party system through a homomorphic encryption rule in a model training process, and the gradient data and the loss data are acquired by the third-party system acquiring corresponding government affair dimension data through the homomorphic encryption rule and the name of the enterprise user so as to train the federal learning model.

Further, still include:

and carrying out sample alignment operation on all the bank dimension data and the government dimension data of the enterprise user according to the asymmetric encryption rule and the name of the enterprise user to obtain the bank dimension data and the government dimension data for carrying out the federal learning model training.

Further, still include:

establishing an initial federal learning model according to a preset federal learning random forest algorithm;

and acquiring and mutually transmitting corresponding gradient data and loss data through a homomorphic encryption rule in the initial federal learning model training convergence process to obtain the federal learning model with the model precision meeting a precision threshold.

Further, still include:

acquiring newly added bank dimension data and government dimension data according to a set time period;

and acquiring and mutually transmitting corresponding gradient data and loss data to obtain an updated federal learning model.

Further, the determining financing admission results of the enterprise users according to the bank dimension data and the federal learning model includes:

and determining the financing admission result of the enterprise user according to the historical admission data, the historical financing data, the loan amount label and the federal learning model in the bank dimension data.

In a second aspect, the present application provides an enterprise financing credit granting device based on federal learning, including:

the bank dimension data determining module is used for receiving a financing request sent by an enterprise user and acquiring corresponding bank dimension data according to the name of the enterprise user;

and the federal learning model financing evaluation module is used for determining financing admission results of the enterprise users according to the bank dimension data and the federal learning model and returning the financing admission results to the enterprise users, wherein the federal learning model acquires corresponding gradient data and loss data from a third-party system through a homomorphic encryption rule in a model training process, and the gradient data and the loss data are acquired by the third-party system through the homomorphic encryption rule and the enterprise user name to acquire corresponding government service dimension data for federal learning model training.

Further, still include:

and the sample data alignment unit is used for performing sample alignment operation on all the bank dimension data and the government affair dimension data of the enterprise user according to the homomorphic encryption rule and the name of the enterprise user to obtain the bank dimension data and the government affair dimension data for performing the federal learning model training.

Further, still include:

the federal learning modeling unit is used for establishing an initial federal learning model according to a preset federal learning random forest algorithm;

and the federal learning training unit is used for acquiring and mutually transmitting corresponding gradient data and loss data through a homomorphic encryption rule in the initial federal learning model training convergence process to obtain the federal learning model with the model precision meeting the precision threshold.

Further, still include:

the newly-added data acquisition unit is used for acquiring the currently-added bank dimension data and government affair dimension data according to a set time period;

and the federal learning model updating unit is used for acquiring and mutually transmitting corresponding gradient data and loss data to obtain an updated federal learning model.

Further, the federal learning model financing evaluation module comprises:

and the bank financing evaluation unit is used for determining the financing admission result of the enterprise user according to the historical admission data, the historical financing data, the loan amount label and the federal learning model in the bank dimension data.

In a third aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the federal learning based enterprise financing credit method when executing the program.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the federal learning based enterprise financing authorization method.

According to the technical scheme, the enterprise financing trust method and the enterprise financing trust device based on the federal learning enable the original data of the enterprise user to be still stored in the local of each system through the gradient data and the loss data of the federal learning model aiming at the same enterprise user among different systems, the exchange of the original data is not carried out in the modeling and model operation processes, all data can be effectively integrated on the premise of guaranteeing the data privacy, and the accuracy of enterprise financing trust is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow chart illustrating an enterprise financing method based on federated learning according to an embodiment of the present application;

FIG. 2 is a second flowchart illustrating an enterprise financing method based on federated learning in an embodiment of the present application;

FIG. 3 is a third schematic flowchart of an enterprise financing authorization method based on federated learning in the embodiment of the present application;

FIG. 4 is a block diagram of an enterprise financing authorization system based on Federal learning according to an embodiment of the present application;

FIG. 5 is a second block diagram of an enterprise financing authorization device based on Federal learning in an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device in an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In consideration of the problems that credit admission and risk prevention and control in the prior art are realized by making rules by business experts and the manner of credit admission and risk prevention and control are difficult to continue only by means of traditional 'expert loan curing' means, the application provides an enterprise financing crediting method and device based on federal learning.

In order to effectively integrate data of each party and improve the accuracy of enterprise financing credit under the premise of guaranteeing data privacy, the application provides an embodiment of an enterprise financing credit granting method based on federal learning, and referring to fig. 1, the enterprise financing credit granting method based on federal learning specifically includes the following contents:

step S101: and receiving a financing request sent by an enterprise user, and acquiring corresponding bank dimension data according to the name of the enterprise user.

Optionally, the enterprise user may log in through a mobile phone terminal of a bank or a financial service window of a government department, and start a loan process after inputting a unique enterprise identifier (enterprise name). According to the method and the system, for example, an enterprise user initiates a financing request to a bank, a bank system can obtain corresponding bank dimension data according to the enterprise user name, wherein the bank dimension data can include historical small and micro enterprise admission and financing records corresponding to the enterprise.

It can be understood that if an enterprise user logs in through a financial service window of a government department, that is, initiates a financing request to the government department, corresponding government dimensional data can also be obtained according to the name of the enterprise user, where the government dimensional data includes, but is not limited to: and the enterprise and business registration, tax rating, electric power payment, social security payment, credit rating and ecological record data corresponding to the enterprise.

Step S102: and determining a financing admission result of the enterprise user according to the bank dimension data and a federal learning model and returning the financing admission result to the enterprise user, wherein the federal learning model acquires corresponding gradient data and loss data from a third-party system through a homomorphic encryption rule in a model training process, and the gradient data and the loss data are acquired by the third-party system acquiring corresponding government affair dimension data through the homomorphic encryption rule and the name of the enterprise user so as to train the federal learning model.

Optionally, when an enterprise user initiates a financing request to a bank, the bank system may determine a financing admission result of the enterprise user by using bank dimension data and a federal learning model and return the financing admission result to the enterprise user, wherein when the federal learning model of the bank system is used for model training, in order to integrate third-party data and improve the financing assessment accuracy, related data of the enterprise user may be acquired from a corresponding third-party system (e.g., one or more government systems), and based on the considerations of data confidentiality and information security, the third-party system does not directly transmit original data to the bank system, but the third-party system also performs financing assessment of the federal learning model on the enterprise user, and transmits generated gradient data and loss data to the bank system in the process, and similarly, the bank system may also transmit gradient data and loss data generated by itself to the bank system, so that the respective local federal learning model is more accurate, namely, multi-party data is integrated.

As can be seen from the above description, the enterprise financing trust method based on federal learning provided in the embodiments of the present application can transmit gradient data and loss data of the federal learning model of the same enterprise user among different systems, so that the original data of the enterprise user is still stored in each system locally, and no exchange of the original data is performed during modeling and model operation, so that data of each party can be effectively integrated on the premise of guaranteeing data privacy, and the accuracy of enterprise financing trust is improved.

In order to enable different systems to perform accurate federal learning model training on the same enterprise user, in an embodiment of the federal learning-based enterprise financing credit method of the present application, the following may be specifically included:

and carrying out sample alignment operation on all the bank dimension data and the government dimension data of the enterprise user according to the asymmetric encryption rule and the name of the enterprise user to obtain the bank dimension data and the government dimension data for carrying out the federal learning model training.

Specifically, the method can perform sample alignment on the government dimensional data and the bank dimensional data of the enterprise history (for example, two years, a month is taken as an observation time point) through the enterprise ID, and complete the ID alignment of the samples by adopting an asymmetric encryption method.

In order to improve the accuracy of financing assessment for enterprise users, in an embodiment of the enterprise financing authorization method based on federal learning according to the present application, referring to fig. 2, the following contents may be further specifically included:

step S201: and establishing an initial federal learning model according to a preset federal learning random forest algorithm.

Step S202: and acquiring and mutually transmitting corresponding gradient data and loss data through a homomorphic encryption rule in the initial federal learning model training convergence process to obtain the federal learning model with the model precision meeting a precision threshold.

Specifically, the method can be used for modeling by using a federal learning random forest algorithm, two sets of parameters of a government end and a bank end are respectively solved, and two ends of the two sets of parameters are respectively held. The homomorphic encryption ensures that original data cannot go out of the local area, the gradient and loss required in the random forest model training convergence process are transmitted through homomorphic encryption, the encryption process is irreversible, and no information leakage is ensured.

It can be understood that the gradient and loss in the process of model training at the government end and the bank end are exchanged by using a homomorphic encryption method, and two corresponding sets of parameters are ensured to be obtained by simultaneously training the two ends. After a threshold for setting model accuracy is met, the model training is considered complete.

In order to improve the evaluation accuracy of the federal learning model, in an embodiment of the federal learning-based enterprise financing authorization method of the present application, referring to fig. 3, the following contents may be further specifically included:

step S301: and acquiring the newly added bank dimension data and government dimension data according to a set time period.

Step S302: and acquiring and mutually transmitting corresponding gradient data and loss data to obtain an updated federal learning model.

Specifically, the job scheduling node at the bank end may update the model at a set time period (e.g., 24 points per day), and perform self-learning update of the model by including the dimensions of the enterprise data at the latest time point. In the self-learning process, the storage and calculation nodes of the bank end and the government end complete the updating of two sets of model parameters (the gradient and loss are exchanged by a homomorphic encryption method).

In order to perform accurate financing evaluation according to bank dimension data, in an embodiment of the enterprise financing authorization method based on federal learning according to the present application, the step S102 may further include the following steps:

and determining the financing admission result of the enterprise user according to the historical admission data, the historical financing data, the loan amount label and the federal learning model in the bank dimension data.

Specifically, while the government dimension data and the bank dimension data exist for the same enterprise user, the two may also be different, for example, a loan amount tag may be included in the bank dimension data in addition to the conventional historical admission data and historical financing data.

In order to effectively integrate data of each party and improve the accuracy of enterprise financing trust on the premise of guaranteeing data privacy, the application provides an embodiment of an enterprise financing trust device based on federal learning, which is used for realizing all or part of the contents of the enterprise financing trust method based on federal learning, and the enterprise financing trust device based on federal learning specifically includes the following contents:

and the bank dimension data determining module 10 is configured to receive a financing request sent by an enterprise user, and obtain corresponding bank dimension data according to the name of the enterprise user.

And the federal learning model financing evaluation module 20 is used for determining financing admission results of the enterprise users according to the bank dimension data and the federal learning model and returning the financing admission results to the enterprise users, wherein the federal learning model acquires corresponding gradient data and loss data from a third-party system through a homomorphic encryption rule in a model training process, and the gradient data and the loss data are acquired by the third-party system through the homomorphic encryption rule and the enterprise user name to acquire corresponding government service dimension data so as to train the federal learning model.

As can be seen from the above description, the enterprise financing trust service device based on federal learning provided in the embodiment of the present application can transmit gradient data and loss data of the federal learning model of the same enterprise user among different systems, so that the original data of the enterprise user is still stored in each system locally, and no exchange of the original data is performed in the modeling and model operation processes, so that data of each party can be effectively integrated on the premise of guaranteeing data privacy, and the accuracy of enterprise financing trust service is improved.

In order to enable different systems to perform accurate federal learning model training on the same enterprise user, in an embodiment of the federal learning-based enterprise financing credit granting device of the present application, the content further includes the following contents:

and the sample data alignment unit is used for performing sample alignment operation on all the bank dimension data and the government affair dimension data of the enterprise user according to the homomorphic encryption rule and the name of the enterprise user to obtain the bank dimension data and the government affair dimension data for performing the federal learning model training.

In order to improve the accuracy of financing assessment for enterprise users, in an embodiment of the federal learning-based enterprise financing credit granting device of the present application, the following contents are further included:

and the federal learning modeling unit is used for establishing an initial federal learning model according to a preset federal learning random forest algorithm.

And the federal learning training unit is used for acquiring and mutually transmitting corresponding gradient data and loss data through a homomorphic encryption rule in the initial federal learning model training convergence process to obtain the federal learning model with the model precision meeting the precision threshold.

In order to improve the evaluation accuracy of the federal learning model, in an embodiment of the federal learning-based enterprise financing credit system, the system further includes the following components:

and the newly-added data acquisition unit is used for acquiring the currently-added bank dimension data and government affair dimension data according to a set time period.

And the federal learning model updating unit is used for acquiring and mutually transmitting corresponding gradient data and loss data to obtain an updated federal learning model.

In order to perform accurate financing assessment according to bank dimension data, in an embodiment of the enterprise financing authorization device based on federal learning according to the present application, referring to fig. 5, the federal learning model financing assessment module 20 includes:

and the bank financing evaluation unit 21 is used for determining the financing admission result of the enterprise user according to the historical admission data, the historical financing data, the loan amount label and the federal learning model in the bank dimension data.

In order to further explain the scheme, the present application further provides a specific application example for implementing the federal learning-based enterprise financing credit granting method by using the federal learning-based enterprise financing credit granting device, which specifically includes the following contents:

1. the method comprises the steps that a federal learning calculation and storage service (node), a job scheduling service (node), a message transmission service (node) and an online reasoning service (node) are deployed in a commercial bank. Wherein the computing and storage service is used for storing historical loan records of small micro-enterprises in commercial banks and simultaneously undertakes subsequent federal learning and training tasks. The job scheduling service is a scheduling task of federal learning training and pre-estimation jobs. The message transmission service helps commercial banks and local big data bureaus (platforms collect local big data, and the big data bureaus in the whole country such as Beijing and the like at present accept the deployment and application of new technologies such as federal learning and the like) to align the ID of each small micro-enterprise through a homomorphic encryption method, so that subsequent calculation is facilitated. And the online reasoning service is used for acquiring data of each dimension of the enterprise in government affairs and the enterprise through the data enterprise name to perform admission and generate a list of financing amount after the model training is finished. The bank node is collectively called a node B.

2. The method comprises the steps of deploying a federally learned calculation and storage service (node) and a message transmission service (node) on a large government data platform. Job scheduling and online reasoning are triggered by the business bank side in a unified way. Government deployed nodes are collectively referred to as G-end nodes. Since then, federal learning services have been implemented in both banks and government deployments.

Second, data storage

1. And storing the corresponding business registration, tax rating, electric power payment, social security payment, credit rating and ecological record data of each enterprise in the big government data platform in the G-end node.

2. And storing the historical micro enterprise admission and financing records corresponding to the enterprises in the bank in the B-end node.

Third, model construction

1. In a vertical federal manner, the business historical (two years, month observation time point) government data and bank data are first aligned by the business ID. And completing ID alignment of the samples by adopting an asymmetric encryption method.

2. Because the G end only has a sample X, the B end has a label Y (loan amount), and both federal learning parties need to construct the model, the gradient and loss in the model training process of the G end and the B end are exchanged by using a homomorphic encryption method, and the two ends are guaranteed to be trained simultaneously to obtain two corresponding sets of parameters. After a threshold for setting model accuracy is met, the model training is considered complete. Modeling is carried out by using a federal learning random forest algorithm, two sets of parameters of A1, A2.. A11 at the G end and A12 and A13 at the B end are respectively solved, and two ends of the two sets of parameters are respectively held. The homomorphic encryption ensures that original data cannot go out of the local area, the gradient and loss required in the random forest model training convergence process are transmitted through homomorphic encryption, the encryption process is irreversible, and no information leakage is ensured. Data of industry and commerce, tax, electric power, social security and the like held by the original G end and bank credit and default labels of the B end are comprehensively considered through the model, and loan admission of small and micro enterprises can be realized.

3. And storing the trained model at the B end and the G end respectively.

Fourth, on-line estimation

1. The small and micro enterprises log in through a bank mobile phone terminal or a financial service window of a government department, and start a loan taking process after inputting an enterprise unique identification (enterprise name).

2. And the B-end job scheduling node updates the model every day (24 points), and performs model self-learning updating by incorporating each dimensionality of the enterprise data at the latest time point. In the self-learning process, the storage computing nodes of the B end and the G end complete the updating of two sets of model parameters (the gradient and loss are exchanged by a homomorphic encryption method).

3. And after receiving the request, the online reasoning node at the B end carries out model reasoning, the model is input according to the dimensional characteristics of the enterprise, wherein the G end inputs characteristic data X1-X11, the B end inputs X12 and X13 (the model is updated to be latest at the moment), the loan admission of each small micro-enterprise is completed, a loan-free white list is generated, and the result is transmitted back to a bank mobile phone end or a financial service window of a government department.

According to the method, the bank is helped to acquire the data representation of the enterprise in each government affair system under the condition of ensuring the data safety through federal learning, so that the popular financial financing service of small and micro enterprises is completed. For banks, loan products are innovated, the popular loan market of high-quality small and micro enterprises is expanded, and the rapid development of services such as settlement and account opening, company and personal deposit and financing is driven.

In terms of hardware, in order to effectively integrate data of each party and improve the accuracy of enterprise financing trust on the premise of guaranteeing data privacy, the present application provides an embodiment of an electronic device for implementing all or part of the contents in the federal learning-based enterprise financing trust method, where the electronic device specifically includes the following contents:

a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the enterprise financing credit authorization device based on federal learning and relevant equipment such as a core business system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiment of the enterprise financing trust method based on federal learning and the embodiment of the enterprise financing trust device based on federal learning in the embodiments, and the contents thereof are incorporated herein, and repeated details are not repeated.

It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

In practical applications, part of the federally learned enterprise financing authorization method may be executed on the electronic device side as described above, or all operations may be completed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.

The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.

Fig. 6 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 6, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 6 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.

In one embodiment, the federal learning based enterprise financing credit granting method function may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:

step S101: and receiving a financing request sent by an enterprise user, and acquiring corresponding bank dimension data according to the name of the enterprise user.

Step S102: and determining a financing admission result of the enterprise user according to the bank dimension data and a federal learning model and returning the financing admission result to the enterprise user, wherein the federal learning model acquires corresponding gradient data and loss data from a third-party system through a homomorphic encryption rule in a model training process, and the gradient data and the loss data are acquired by the third-party system acquiring corresponding government affair dimension data through the homomorphic encryption rule and the name of the enterprise user so as to train the federal learning model.

As can be seen from the above description, according to the electronic device provided in the embodiment of the present application, gradient data and loss data of the federal learning model of the same enterprise user are mutually transmitted between different systems, so that the original data of the enterprise user is still stored locally in each system, and exchange of the original data is not performed in the modeling and model operation processes, so that data of each party can be effectively integrated on the premise of guaranteeing data privacy, and the accuracy of enterprise financing and credit granting is improved.

In another embodiment, the federal learning based enterprise financing credit granting device may be configured separately from the central processor 9100, for example, the federal learning based enterprise financing credit granting device may be configured as a chip connected to the central processor 9100, and the function of the federal learning based enterprise financing credit granting method may be implemented by the control of the central processor.

As shown in fig. 6, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 6; further, the electronic device 9600 may further include components not shown in fig. 6, which may be referred to in the art.

As shown in fig. 6, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.

The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.

The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.

The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.

The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).

The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.

An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the federal learning based enterprise financing authorization method for a server or a client as an execution subject in the above embodiments, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the federal learning based enterprise financing authorization method for a server or a client as an execution subject, for example, the processor implements the following steps when executing the computer program:

step S101: and receiving a financing request sent by an enterprise user, and acquiring corresponding bank dimension data according to the name of the enterprise user.

Step S102: and determining a financing admission result of the enterprise user according to the bank dimension data and a federal learning model and returning the financing admission result to the enterprise user, wherein the federal learning model acquires corresponding gradient data and loss data from a third-party system through a homomorphic encryption rule in a model training process, and the gradient data and the loss data are acquired by the third-party system acquiring corresponding government affair dimension data through the homomorphic encryption rule and the name of the enterprise user so as to train the federal learning model.

As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application, through the mutual transmission of gradient data and loss data of the federal learning model of the same enterprise user among different systems, enables the original data of the enterprise user to be still stored locally in each system, and does not exchange the original data in the modeling and model operation processes, so that on the premise of guaranteeing data privacy, data of each party can be effectively integrated, and the accuracy of enterprise financing and credit extension is improved.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.