A method, device and system for identifying collusion risk for multi-party collaboration

Abstract

The present invention discloses a collusion risk identification method, device and system for multi-party collaboration, which relates to the field of network security technology. The method includes: respectively obtaining a first model parameter set of a first user and a second model parameter set of a second user; for each model parameter set, determining the variance value of the model parameter set based on multiple parameter subsets in the model parameter set; determining the covariance value of the first model parameter set and the second model parameter set; determining the correlation coefficient between the first model parameter set and the second model parameter set according to the variance value and the covariance value; determining the collusion risk judgment value between the model parameter set of each user and other model parameter sets according to the correlation coefficient; when there is a target user among multiple users whose collusion risk judgment value does not meet the preset conditions, stopping the operation of using the model parameter set provided by the target user for multi-party collaborative model training. This solution avoids malicious users from maliciously tampering with the trained local model parameters.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular to a method, device and system for identifying collusion risks for multi-party collaboration. Background Art

[0002] In related technologies, in order to train more powerful AI models, sufficient training data is often required. When the amount of training data is limited, multi-party collaborative training models such as federated learning and collaborative learning are often used to leverage the power of more individuals to expand the data scale. However, during model training using multi-party collaborative training models, malicious users can arbitrarily tamper with the trained local model for their own purposes. This can cause the malicious modifications contained in the local model to spread to the final model and the local models of other individuals, affecting the normal application of the final model. Summary of the Invention

[0003] In order to overcome the problems existing in the related art, the present invention provides a collusion risk identification method, device and system for multi-party collaboration.

[0004] According to a first aspect of an embodiment of the present invention, a method for identifying collusion risk for multi-party collaboration is provided, comprising:

[0005] Obtaining, at a first preset time node, a first model parameter set of a first user and a second model parameter set of a second user, respectively; the first user and the second user are any two users among a plurality of users participating in multi-party collaborative model training; the first model parameter set and the second model parameter set are both model parameters used for performing the multi-party collaborative model training;

[0006] For each of the first model parameter set and the second model parameter set, determining a variance value of the model parameter set based on multiple parameter subsets in the model parameter set; each parameter subset in the multiple parameter subsets includes model parameters corresponding to multiple adjacent training rounds of the multi-party collaborative model training before the first preset time node;

[0007] determining a covariance value between the first model parameter set and the second model parameter set;

[0008] determining a correlation coefficient between the first model parameter set and the second model parameter set based on the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value;

[0009] When correlation coefficients corresponding to any two users among the multiple users are obtained, determining a collusion risk determination value between a model parameter set corresponding to each user among the multiple users and other model parameter sets according to the multiple correlation coefficients;

[0010] In the event that there is a target user among the multiple users whose collusion risk determination value does not meet a preset condition, the operation of using the model parameter set provided by the target user for the multi-party collaborative model training is stopped.

[0011] In some embodiments of the present invention, upon obtaining correlation coefficients corresponding to any two users among the multiple users, determining, based on the multiple correlation coefficients, a collusion risk determination value between a model parameter set corresponding to each user among the multiple users and other model parameter sets includes:

[0012] When correlation coefficients corresponding to any two users among the plurality of users are obtained, generating a collusion risk matrix Ψ according to the plurality of correlation coefficients;

[0013] in, n is the number of users, r is the correlation coefficient, and r(1, n) represents the correlation coefficient between the model parameter set corresponding to the first user and the model parameter set corresponding to the nth user;

[0014] The following formula is used to sum the rows of Ψ to obtain the judgment value corresponding to each user:

[0015]

[0016] Among them, θ k is the decision value corresponding to the k-th user.

[0017] In some embodiments of the present invention, when there is a target user among the multiple users whose collusion risk determination value does not meet a preset condition, the operation of stopping using the model parameter set provided by the target user for the multi-party collaborative model training includes:

[0018] For each of the multiple users, comparing the determination value corresponding to the user with a preset collusion risk threshold to obtain a comparison result;

[0019] When the comparison result shows that the determination value is greater than the collusion risk threshold, the user is determined to be the target user, and the operation of using the model parameter set provided by the target user for the multi-party collaborative model training is stopped.

[0020] In some embodiments of the present invention, determining the correlation coefficient between the first model parameter set and the second model parameter set based on the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value includes:

[0021] The correlation coefficient between the first model parameter set and the second model parameter set is calculated according to the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value by the following formula:

[0022]

[0023] Wherein, r(l,k) is the correlation coefficient between the first model parameter set corresponding to user l and the second model parameter set corresponding to user k, Cov(l,k) is the covariance value between the first model parameter set and the second model parameter set, is the variance value of the first model parameter set, is the variance value of the second model parameter set.

[0024] In some embodiments of the present invention, determining a covariance value of the first model parameter set and the second model parameter set includes:

[0025] The covariance value of the first model parameter set and the second model parameter set is calculated by the following formula:

[0026]

[0027] Wherein, Cov(l,k) is the covariance value between the first model parameter set corresponding to user l and the second model parameter set corresponding to user k, is the i-th parameter subset in the first model parameter set, is the i-th parameter subset in the second model parameter set, is the mean of the first model parameter set, is the mean of the second model parameter set.

[0028] In some embodiments of the present invention, determining, for each of the first model parameter set and the second model parameter set, a variance value of the model parameter set based on multiple parameter subsets in the model parameter set, includes:

[0029] For each model parameter set in the first model parameter set and the second model parameter set, the mean of the model parameter set is calculated using the following formula:

[0030]

[0031] in, is the mean of the kth set of model parameters, is the i-th parameter subset in the k-th model parameter set, and Δt is the number of parameter subsets;

[0032] The variance value of the model parameter set is calculated by the following formula:

[0033]

[0034] in, is the variance value of the kth model parameter set.

[0035] According to a second aspect of an embodiment of the present invention, a collusion risk identification device for multi-party collaboration is provided, comprising:

[0036] an acquisition unit, configured to respectively acquire, at a first preset time node, a first model parameter set of a first user and a second model parameter set of a second user; the first user and the second user being any two users among a plurality of users participating in multi-party collaborative model training; the first model parameter set and the second model parameter set being model parameters used for performing the multi-party collaborative model training;

[0037] a first determining unit, configured to determine, for each of the first model parameter set and the second model parameter set, a variance value of the model parameter set based on a plurality of parameter subsets in the model parameter set, wherein each parameter subset includes model parameters corresponding to a plurality of adjacent training rounds of the multi-party collaborative model training before the first preset time node;

[0038] a second determining unit, configured to determine a covariance value between the first model parameter set and the second model parameter set;

[0039] a third determining unit, configured to determine a correlation coefficient between the first model parameter set and the second model parameter set according to the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value;

[0040] a fourth determining unit, configured to, upon obtaining correlation coefficients corresponding to any two users among the plurality of users, determine, based on the plurality of correlation coefficients, a collusion risk determination value between a model parameter set corresponding to each user among the plurality of users and other model parameter sets;

[0041] The stopping unit is configured to stop using the model parameter set provided by the target user for the multi-party collaborative model training if there is a target user among the multiple users whose collusion risk determination value does not meet a preset condition.

[0042] According to a third aspect of an embodiment of the present invention, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of the first aspects is implemented.

[0043] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the first aspects is implemented.

[0044] According to a fifth aspect of an embodiment of the present invention, a computer program product is provided, comprising a computer program, wherein the computer program implements the method according to any one of the first aspects when executed by a processor.

[0045] The technical solution provided by the embodiment of the present invention may include the following beneficial effects: by respectively obtaining a first model parameter set of a first user and a second model parameter set of a second user at a first preset time node; for each model parameter set in the first model parameter set and the second model parameter set, determining a variance value of the model parameter set based on multiple parameter subsets in the model parameter set; determining a covariance value of the first model parameter set and the second model parameter set; determining a correlation coefficient between the first model parameter set and the second model parameter set according to the variance value corresponding to the first model parameter set, the variance value and the covariance value corresponding to the second model parameter set; and in the case of obtaining the correlation coefficients corresponding to any two users among a plurality of users, In this case, the collusion risk judgment value between the model parameter set corresponding to each user among multiple users and other model parameter sets is determined according to multiple correlation coefficients; when there is a target user among multiple users whose collusion risk judgment value does not meet the preset conditions, the model parameter set provided by the target user is stopped from being used for multi-party collaborative model training. That is, by calculating the correlation degree of model parameters uploaded by different users and setting a specific threshold, users with higher collusion risk whose correlation degree exceeds the threshold can be identified, thereby effectively avoiding the spread of malicious tampering data contained in the local model to the final model and the local models of other individuals, thereby improving the security of multi-party collaborative model training.

[0046] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0048] Figure 1 The present invention is a flowchart of a method for identifying collusion risks for multi-party collaboration according to an exemplary embodiment.

[0049] Figure 2 The present invention is a block diagram of a collusion risk identification device for multi-party collaboration according to an exemplary embodiment.

[0050] Figure 3 The present invention is a block diagram of an apparatus for a method for identifying collusion risk for multi-party collaboration according to an exemplary embodiment. DETAILED DESCRIPTION

[0051] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0052] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present invention. The singular forms "a", "an" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0053] It should be understood that although the terms "first," "second," "third," etc. may be used to describe various information in embodiments of the present invention, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, without departing from the scope of embodiments of the present invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the terms "if" and "if" as used herein may be interpreted as "at the time of," "when," or "in response to a determination."

[0054] Furthermore, the various forms of processes shown in the embodiments of the present invention may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. This is not a limitation herein.

[0055] As artificial intelligence (AI) technology increasingly becomes part of our daily lives, data resources supporting its continuous enhancement and improvement are becoming increasingly important. In the current development of AI, more training data can produce more powerful AI models. However, the training data that a single individual can provide is often limited. Consequently, interest has arisen in multi-party collaborative training models, such as federated learning and collaborative learning, which attempt to leverage the power of more individuals to expand the data pool. Against this backdrop, the risk of collusion within multi-party collaboration has become increasingly prominent. During collaborative model training, malicious users can arbitrarily tamper with the trained local model for their own purposes (e.g., through poisoning attacks and backdoor attacks). This malicious modification, contained in the local model, can spread to the final model and other individuals' local models, creating hidden dangers for subsequent model applications in daily life. This malicious tampering capability becomes even more powerful in the presence of multi-party collusion, posing a more severe model security threat and reducing the social benefits of AI technology.

[0056] In order to solve the above problems, the present invention provides a collusion risk identification method, device and system for multi-party collaboration, which respectively obtains a first model parameter set of a first user and a second model parameter set of a second user at a first preset time node; for each model parameter set of the first model parameter set and the second model parameter set, determines the variance value of the model parameter set based on multiple parameter subsets in the model parameter set; determines the covariance value of the first model parameter set and the second model parameter set; determines the correlation coefficient between the first model parameter set and the second model parameter set based on the variance value corresponding to the first model parameter set and the variance value and covariance value corresponding to the second model parameter set; and obtains the variance value corresponding to any two users among the multiple users. In the case of a correlation coefficient, the collusion risk judgment value between the model parameter set corresponding to each user among multiple users and other model parameter sets is determined according to multiple correlation coefficients; when there is a target user among multiple users whose collusion risk judgment value does not meet the preset conditions, the model parameter set provided by the target user is stopped from being used for multi-party collaborative model training. That is, by calculating the correlation degree of model parameters uploaded by different users and setting a specific threshold, users with higher collusion risks whose correlation degree exceeds the threshold can be identified, thereby effectively avoiding the spread of malicious tampering data contained in the local model to the final model and the local models of other individuals, thereby improving the security of multi-party collaborative model training.

[0057] It should be noted that collaborative learning technologies, such as federated learning, are the foundation of multi-party collaborative model training. Their goal is to combine training data from multiple parties while respecting privacy, searching within the parameter space for parameter configurations that minimize the objective function within a larger data space. In practical applications, collaborative optimization techniques, to avoid the privacy risks associated with directly sharing raw data, distribute the optimization steps using data training to participating users locally. These locally trained models are then aggregated using a corresponding aggregation algorithm, thereby achieving information sharing. This collaborative training model can find parameters that meet requirements within a larger data space, resulting in excellent performance. Specifically, in multi-party collaborative optimization, the aggregator node has no access to the raw data itself. Instead, it distributes the optimized model to participating users locally. Participating users optimize the distributed model using their local data and then upload the optimized results. After receiving the models uploaded by each participant, the aggregator node aggregates the information using a specific algorithm. Because the locally optimized model carries local data information, this aggregation algorithm can output a collaborative optimization result that incorporates data from multiple parties.

[0058] During this process, the aggregator can only observe the resulting model uploaded by the participating nodes and has no access to the original data. This training method, which does not allow direct access to the original data, enhances the privacy and security of the training. However, it also weakens the aggregator's ability to identify participating users, making it unable to detect malicious nodes among participating users. This also places higher demands on the aggregator's information screening capabilities.

[0059] Correlation testing is a common theoretical analysis tool in statistics. Its purpose is to analyze the degree of correlation between different random variables using the statistical properties of data. It then uses this correlation to perform subsequent tasks and establish causal relationships between the corresponding variables. In correlation testing, the covariance between random variables measures the degree of correlation between the two random variables. The covariance between two random variables with a stronger correlation is also greater. Furthermore, for comparison purposes, correlation tests often use the variance of the random variables to remove the influence of their size, converting the covariance value into a correlation coefficient. In practical applications, the correlation coefficient is a number with an absolute value less than 1 and is unaffected by the size of the random variables. Therefore, it can be used to compare the correlation between different random variables.

[0060] Figure 1 is a flow chart of a method for identifying collusion risk for multi-party collaboration according to an exemplary embodiment. Figure 1 As shown, it should be noted that the collusion risk identification method for multi-party collaboration in the embodiment of the present invention is applied to the collusion risk identification device for multi-party collaboration. Figure 1 As shown, the method may include the following steps:

[0061] Step 101 : At a first preset time point, a first model parameter set of a first user and a second model parameter set of a second user are respectively obtained.

[0062] The first user and the second user are any two users among multiple users participating in the multi-party collaborative model training; the first model parameter set and the second model parameter set are both model parameters used for multi-party collaborative model training.

[0063] It should be noted that according to the multi-party collaborative optimization method, the local training processes of each participating user should be independent of each other, so there is no correlation between them, and the correlation coefficient should be close to 0. Therefore, the size of the correlation coefficient between the first model parameter set of the first user and the second model parameter set of the second user can be used to determine whether there is a collusion risk, that is, whether there is a situation in which a user maliciously tampered with the model parameters.

[0064] In one embodiment, since the multi-party collaborative model training requires multiple rounds of training, the correlation coefficient may be calculated every n rounds according to the total number of rounds of model training.

[0065] In addition, since there are multiple users participating in the multi-party collaborative model training, it is necessary to calculate the correlation coefficient for any two users among the multiple users, so as to eventually find all users who have maliciously tampered with the model parameters.

[0066] In some embodiments of the present invention, before step 101, time slot division may be performed based on the total number of rounds T of multi-party collaborative training, where the same number of rounds is divided into m time slots, each time slot having an interval of Δt, where the relationship between the variables is:

[0067] T=m*Δt

[0068] Here, m can be set according to actual needs.

[0069] Step 102 : For each of the first model parameter set and the second model parameter set, determine a variance value of the model parameter set based on a plurality of parameter subsets in the model parameter set.

[0070] Each parameter subset among the multiple parameter subsets includes model parameters corresponding to multiple adjacent training rounds of multi-party collaborative model training before the first preset time node.

[0071] In one embodiment, the first set of model parameters may be As a parameter subset, the second model parameter set can be A subset of parameters.

[0072] As an example, the model parameters uploaded by user k, k∈{1,2,…,n}, can be collected in each round of model training. 1≤i≤Δt, add it to the corresponding set, that is:

[0073]

[0074] In some embodiments of the present invention, step 102 may specifically include the following steps:

[0075] For each model parameter set in the first model parameter set and the second model parameter set, the mean of the model parameter set is calculated by the following formula:

[0076]

[0077] in, is the mean of the k-th model parameter set, is the i-th parameter subset in the k-th model parameter set, and Δt is the number of parameter subsets;

[0078] The variance value of the model parameter set is calculated using the following formula:

[0079]

[0080] in, is the variance value of the kth model parameter set.

[0081] Step 103: Determine the covariance value of the first model parameter set and the second model parameter set.

[0082] In some embodiments of the present invention, step 103 may specifically include the following steps:

[0083] The covariance value of the first model parameter set and the second model parameter set is calculated by the following formula:

[0084]

[0085] Where Cov(l,k) is the covariance value between the first model parameter set corresponding to user l and the second model parameter set corresponding to user k, is the i-th parameter subset in the first model parameter set, is the i-th parameter subset in the second model parameter set, is the mean of the first model parameter set, is the mean of the second model parameter set.

[0086] Step 104 : performing normalization calculation based on the variance value corresponding to the first model parameter set, the variance value and the covariance value corresponding to the second model parameter set to determine the correlation coefficient between the first model parameter set and the second model parameter set.

[0087] In some embodiments of the present invention, step 104 may specifically include the following steps:

[0088] According to the variance value corresponding to the first model parameter set, the variance value and covariance value corresponding to the second model parameter set, the correlation coefficient between the first model parameter set and the second model parameter set is calculated by the following formula:

[0089]

[0090] Where r(l,k) is the correlation coefficient between the first model parameter set corresponding to user l and the second model parameter set corresponding to user k, Cov(l,k) is the covariance value between the first model parameter set and the second model parameter set, is the variance value of the first model parameter set, is the variance value of the second model parameter set.

[0091] Step 105 , when correlation coefficients corresponding to any two users among the multiple users are obtained, determine the collusion risk determination value between the model parameter set corresponding to each user among the multiple users and other model parameter sets based on the multiple correlation coefficients.

[0092] In some embodiments of the present invention, step 105 may specifically include the following steps:

[0093] When correlation coefficients corresponding to any two users among a plurality of users are obtained, a collusion risk matrix Ψ is generated according to the plurality of correlation coefficients;

[0094] in, n is the number of users, r is the correlation coefficient, r(1,n) represents the correlation coefficient between the model parameter set u corresponding to the first user and the model parameter set corresponding to the nth user;

[0095] The following formula is used to sum the rows of Ψ to obtain the judgment value corresponding to each user:

[0096]

[0097] Among them, θ k is the decision value corresponding to the k-th user.

[0098] Step 106 , when there is a target user among multiple users whose collusion risk determination value does not meet the preset conditions, the operation of using the model parameter set provided by the target user for multi-party collaborative model training is stopped.

[0099] In some embodiments of the present invention, step 106 may specifically include the following steps:

[0100] For each of the multiple users, the judgment value corresponding to the user is compared with a preset collusion risk threshold to obtain a comparison result;

[0101] When the comparison result shows that the judgment value is greater than the collusion risk threshold, the user is determined to be the target user, and the operation of using the model parameter set provided by the target user for multi-party collaborative model training is stopped.

[0102] It should be noted that the above-mentioned collusion risk threshold can be pre-set according to actual needs. In addition, the collusion risk threshold can be dynamically adjusted according to the user's tolerance for collusion risk to achieve the purpose of safe and full use of data information.

[0103] In one embodiment, ε is a preset collusion risk threshold. For user k, if the judgment value θ is satisfied, k >ε, the model parameters uploaded by user k will not participate in the aggregation operation in the next Δt rounds of multi-party collaborative model training.

[0104] In some embodiments of the present invention, after step 106 , if the current round is less than the total rounds of multi-party collaborative model training, the process returns to step 101 until the current round is equal to the total rounds of multi-party collaborative model training.

[0105] In this embodiment of the present invention, leveraging the statistical principle of correlation testing, uploaded model parameters are treated as samples of the same random variable, transforming the problem of identifying collusion risk into a matter of determining the magnitude of correlation. By calculating the correlation coefficient, users can compare it with a specific threshold and determine the collusion risk in the uploaded model parameters. Based on the collusion risk, specific parameters can be excluded from subsequent information aggregation algorithms, thereby achieving the goal of protecting collaborative training.

[0106] According to an embodiment of the present invention, a collusion risk identification method for multi-party collaboration is proposed. A first model parameter set of a first user and a second model parameter set of a second user are obtained at a first preset time node. For each of the first and second model parameter sets, a variance value of the model parameter set is determined based on multiple parameter subsets in the model parameter set. A covariance value of the first and second model parameter sets is determined. A correlation coefficient between the first and second model parameter sets is determined based on the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value corresponding to the second model parameter set. Upon obtaining the correlation coefficients corresponding to any two of the multiple users, a collusion risk determination value is determined between the model parameter set corresponding to each of the multiple users and the other model parameter sets based on the multiple correlation coefficients. If a target user exists among the multiple users whose collusion risk determination value does not meet preset conditions, the model parameter set provided by the target user is stopped from being used for multi-party collaborative model training. This effectively prevents maliciously tampered data contained in the local model from spreading to the final model and the local models of other individuals, thereby improving the security of multi-party collaborative model training.

[0107] Figure 2 This is a block diagram of a device for identifying collusion risk for multi-party collaboration according to an exemplary embodiment. Figure 2 The device includes an acquiring unit 201 , a first determining unit 202 , a second determining unit 203 , a third determining unit 204 , a fourth determining unit 205 and a stopping unit 206 .

[0108] The acquisition unit 201 is configured to respectively acquire a first model parameter set of a first user and a second model parameter set of a second user at a first preset time node; the first user and the second user are any two users among a plurality of users participating in multi-party collaborative model training; the first model parameter set and the second model parameter set are both model parameters used for multi-party collaborative model training;

[0109] A first determining unit 202 is configured to determine, for each of the first model parameter set and the second model parameter set, a variance value of the model parameter set based on multiple parameter subsets in the model parameter set, each of the multiple parameter subsets including model parameters corresponding to multiple adjacent training rounds of the multi-party collaborative model training before a first preset time node;

[0110] A second determining unit 203 is configured to determine a covariance value between the first model parameter set and the second model parameter set;

[0111] A third determining unit 204 is configured to determine a correlation coefficient between the first model parameter set and the second model parameter set based on the variance value corresponding to the first model parameter set and the variance value and covariance value corresponding to the second model parameter set;

[0112] A fourth determining unit 205 is configured to determine, based on the plurality of correlation coefficients, a collusion risk determination value between a model parameter set corresponding to each user and other model parameter sets in the plurality of users, when correlation coefficients corresponding to any two users among the plurality of users are obtained;

[0113] The stopping unit 206 is configured to stop using the model parameter set provided by the target user for multi-party collaborative model training when there is a target user among multiple users whose collusion risk determination value does not meet a preset condition.

[0114] In some embodiments of the present invention, the fourth determining unit 205 may be specifically configured to:

[0115] When correlation coefficients corresponding to any two users among a plurality of users are obtained, a collusion risk matrix Ψ is generated according to the plurality of correlation coefficients;

[0116] in, n is the number of users, r is the correlation coefficient, and r(1, n) represents the correlation coefficient between the model parameter set corresponding to the first user and the model parameter set corresponding to the nth user;

[0117] The following formula is used to sum the rows of Ψ to obtain the judgment value corresponding to each user:

[0118]

[0119] Among them, θ k is the decision value corresponding to the k-th user.

[0120] In some embodiments of the present invention, the stopping unit 206 may be specifically configured to:

[0121] For each of the multiple users, the judgment value corresponding to the user is compared with a preset collusion risk threshold to obtain a comparison result;

[0122] When the comparison result shows that the judgment value is greater than the collusion risk threshold, the user is determined to be the target user, and the operation of using the model parameter set provided by the target user for multi-party collaborative model training is stopped.

[0123] In some embodiments of the present invention, the third determining unit 204 may be specifically configured to:

[0124] According to the variance value corresponding to the first model parameter set, the variance value and covariance value corresponding to the second model parameter set, the correlation coefficient between the first model parameter set and the second model parameter set is calculated by the following formula:

[0125]

[0126] Where r(l,k) is the correlation coefficient between the first model parameter set corresponding to user l and the second model parameter set corresponding to user k, Cov(l,k) is the covariance value between the first model parameter set and the second model parameter set, is the variance value of the first model parameter set, is the variance value of the second model parameter set.

[0127] In some embodiments of the present invention, the second determining unit 203 may be specifically configured to:

[0128] The covariance value of the first model parameter set and the second model parameter set is calculated by the following formula:

[0129]

[0130] Where Cov(l,k) is the covariance value between the first model parameter set corresponding to user l and the second model parameter set corresponding to user k, is the i-th parameter subset in the first model parameter set, is the i-th parameter subset in the second model parameter set, is the mean of the first model parameter set, is the mean of the second model parameter set.

[0131] In some embodiments of the present invention, the first determining unit 202 may be specifically configured to:

[0132] For each model parameter set in the first model parameter set and the second model parameter set, the mean of the model parameter set is calculated by the following formula:

[0133]

[0134] in, is the mean of the k-th model parameter set, is the i-th parameter subset in the k-th model parameter set, and Δt is the number of parameter subsets;

[0135] The variance value of the model parameter set is calculated using the following formula:

[0136]

[0137] in, is the variance value of the kth model parameter set.

[0138] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0139] According to an embodiment of the present invention, a collusion risk identification device for multi-party collaboration is proposed. A first model parameter set of a first user and a second model parameter set of a second user are obtained at a first preset time node. For each of the first and second model parameter sets, a variance value of the model parameter set is determined based on multiple parameter subsets in the model parameter set. A covariance value of the first and second model parameter sets is determined. A correlation coefficient between the first and second model parameter sets is determined based on the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value corresponding to the second model parameter set. Upon obtaining the correlation coefficients corresponding to any two of the multiple users, a collusion risk determination value is determined between the model parameter set corresponding to each of the multiple users and the other model parameter sets based on the multiple correlation coefficients. If a target user exists among the multiple users whose collusion risk determination value does not meet preset conditions, the model parameter set provided by the target user is stopped from being used for multi-party collaborative model training. This effectively prevents maliciously tampered data contained in the local model from spreading to the final model and the local models of other individuals, thereby improving the security of multi-party collaborative model training.

[0140] Figure 3 This is a block diagram of an apparatus for a method for identifying collusion risk for multi-party collaboration, according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0141] Reference Figure 3 , apparatus 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0142] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 302 may include one or more modules to facilitate interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate interaction between the multimedia component 308 and the processing component 302.

[0143] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0144] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 300.

[0145] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0146] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC) that is configured to receive external audio signals when the device 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 304 or transmitted via the communication component 316. In some embodiments, the audio component 310 further includes a speaker for outputting audio signals.

[0147] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0148] The sensor assembly 314 includes one or more sensors for providing various aspects of the status assessment of the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300. The sensor assembly 314 can also detect changes in the position of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and temperature changes of the device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0149] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0150] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0151] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by the processor 320 of the apparatus 300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0152] In an exemplary embodiment, a computer program product is also provided, comprising a computer program, which implements the above method when executed by the processor 320 of the apparatus 300 .

[0153] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow from the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0154] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for identifying collusion risk for multi-party collaboration, characterized by: include: At a first preset time node, respectively obtain a first model parameter set of a first user and a second model parameter set of a second user; the first user and the second user are any two users among a plurality of users participating in the multi-party collaborative model training; The first model parameter set and the second model parameter set are both model parameters used for performing the multi-party collaborative model training; For each of the first model parameter set and the second model parameter set, determining a variance value of the model parameter set based on a plurality of parameter subsets in the model parameter set; Each parameter subset of the multiple parameter subsets includes model parameters corresponding to multiple adjacent training rounds of the multi-party collaborative model training before the first preset time node; determining a covariance value between the first model parameter set and the second model parameter set; determining a correlation coefficient between the first model parameter set and the second model parameter set based on the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value; When correlation coefficients corresponding to any two users among the multiple users are obtained, determining a collusion risk determination value between a model parameter set corresponding to each user among the multiple users and other model parameter sets according to the multiple correlation coefficients; In the event that there is a target user among the multiple users whose collusion risk determination value does not meet a preset condition, the operation of using the model parameter set provided by the target user for the multi-party collaborative model training is stopped.

2. The method for identifying collusion risk for multi-party collaboration according to claim 1 is characterized in that: The step of determining, in a case where correlation coefficients corresponding to any two users among the multiple users are obtained, a collusion risk determination value between a model parameter set corresponding to each user among the multiple users and other model parameter sets according to the multiple correlation coefficients, includes: When correlation coefficients corresponding to any two users among the plurality of users are obtained, generating a collusion risk matrix Ψ according to the plurality of correlation coefficients; in, n is the number of users, r is the correlation coefficient, and r(1, n) represents the correlation coefficient between the model parameter set corresponding to the first user and the model parameter set corresponding to the nth user; The following formula is used to sum the rows of Ψ to obtain the judgment value corresponding to each user: Among them, θ k is the decision value corresponding to the kth user.

3. The method for identifying collusion risk for multi-party collaboration according to claim 1 is characterized in that: When there is a target user among the multiple users whose collusion risk determination value does not meet a preset condition, the operation of stopping using the model parameter set provided by the target user for the multi-party collaborative model training includes: For each of the multiple users, comparing the determination value corresponding to the user with a preset collusion risk threshold to obtain a comparison result; When the comparison result shows that the determination value is greater than the collusion risk threshold, the user is determined to be the target user, and the operation of using the model parameter set provided by the target user for the multi-party collaborative model training is stopped.

4. The method for identifying collusion risk for multi-party collaboration according to claim 1 is characterized in that: The determining the correlation coefficient between the first model parameter set and the second model parameter set according to the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value includes: The correlation coefficient between the first model parameter set and the second model parameter set is calculated according to the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value by the following formula: Wherein, r(l,k) is the correlation coefficient between the first model parameter set corresponding to user l and the second model parameter set corresponding to user k, Cov(l,k) is the covariance value between the first model parameter set and the second model parameter set, is the variance value of the first model parameter set, is the variance value of the second model parameter set.

5. The method for identifying collusion risk for multi-party collaboration according to claim 1 is characterized in that: Determining a covariance value between the first model parameter set and the second model parameter set includes: The covariance value of the first model parameter set and the second model parameter set is calculated by the following formula: Wherein, Cov(l,k) is the covariance value between the first model parameter set corresponding to user l and the second model parameter set corresponding to user k, is the i-th parameter subset in the first model parameter set, is the i-th parameter subset in the second model parameter set, is the mean of the first model parameter set, is the mean of the second model parameter set.

6. The method for identifying collusion risk for multi-party collaboration according to claim 1 is characterized in that: The determining, for each of the first model parameter set and the second model parameter set, a variance value of the model parameter set based on a plurality of parameter subsets in the model parameter set, comprises: For each model parameter set in the first model parameter set and the second model parameter set, the mean of the model parameter set is calculated using the following formula: in, is the mean of the kth set of model parameters, is the i-th parameter subset in the k-th model parameter set, and Δt is the number of parameter subsets; The variance value of the model parameter set is calculated by the following formula: in, is the variance value of the kth model parameter set.

7. A collusion risk identification device for multi-party collaboration, characterized in that: include: An acquisition unit, configured to respectively acquire a first model parameter set of a first user and a second model parameter set of a second user at a first preset time node; the first user and the second user being any two users among a plurality of users participating in the multi-party collaborative model training; The first model parameter set and the second model parameter set are both model parameters used for performing the multi-party collaborative model training; a first determining unit, configured to determine, for each model parameter set in the first model parameter set and the second model parameter set, a variance value of the model parameter set based on a plurality of parameter subsets in the model parameter set; Each parameter subset of the multiple parameter subsets includes model parameters corresponding to multiple adjacent training rounds of the multi-party collaborative model training before the first preset time node; a second determining unit, configured to determine a covariance value between the first model parameter set and the second model parameter set; a third determining unit, configured to determine a correlation coefficient between the first model parameter set and the second model parameter set according to the variance value corresponding to the first model parameter set, the variance value corresponding to the second model parameter set, and the covariance value; a fourth determining unit, configured to, upon obtaining correlation coefficients corresponding to any two users among the plurality of users, determine, based on the plurality of correlation coefficients, a collusion risk determination value between a model parameter set corresponding to each user among the plurality of users and other model parameter sets; The stopping unit is configured to stop using the model parameter set provided by the target user for the multi-party collaborative model training if there is a target user among the multiple users whose collusion risk determination value does not meet a preset condition.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that The computer program implements the method according to any one of claims 1 to 6 when executed by a processor.

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