Abstract: Disclosed are a neural network model update method and device, and a computer storage medium. The method includes: randomly generating a preset number of sub-neural network models as nodes in a blockchain; using a ring signature to share a local data set in the blockchain, and uniformly dividing a data set in the blockchain to generate a training set and a test set; training each node through the training set to generate a trained model, packaging the trained model as a model transaction and sharing the model transaction in the blockchain; using the test set selected by voting to test the model transaction and generating a test result; when the test result is greater than a benchmark evaluation, taking the sub-neural network model corresponding to the test result as a valid vote; and voting a previous block corresponding to the valid vote, selecting a consistent block, and updating all nodes.

Abstract: Disclosed is a method for constructing an auditable and privacy-preserving collaborative deep learning platform based on a blockchain-empowered incentive mechanism, which allows trainers of multiple similar models to cooperate for training deep learning models while protecting confidentiality and auditing correctness of shared parameters. The invention has the following technical effects. Firstly, the encryption method used by model trainers protects the confidentiality of sharing parameters; furthermore, the updated parameters are decrypted through the cooperation of all participants, which reduces the possible disclosure of parameters. Secondly, the encrypted parameters are stored in the blockchain, and are only available to participants and authorized miners who are responsible to update parameters.

Abstract: Disclosed are a data file distribution method and equipment, a smart device and a computer storage medium. The method includes the following operations: sorting data files according to an access frequency of each data file, a sorting mode including an ascending order or a descending order; dividing the data files into at least two data blocks according to a sorted order, numbers of data files in the at least two data blocks being equal; merging the data files in each of the at least two data blocks in pairs to update the data files; sorting the updated data files according to the access frequency of each data file until the numbers of the data files are equal to numbers of distributed nodes; and placing the data files on corresponding distributed nodes.

Abstract: Disclosed are a neural network model update method and device, and a computer storage medium. The method includes: randomly generating a preset number of sub-neural network models as nodes in a blockchain; using a ring signature to share a local data set in the blockchain, and uniformly dividing a data set in the blockchain to generate a training set and a test set; training each node through the training set to generate a trained model, packaging the trained model as a model transaction and sharing the model transaction in the blockchain; using the test set selected by voting to test the model transaction and generating a test result; when the test result is greater than a benchmark evaluation, taking the sub-neural network model corresponding to the test result as a valid vote; and voting a previous block corresponding to the valid vote, selecting a consistent block, and updating all nodes.

Abstract: Disclosed is a method for constructing an auditable and privacy-preserving collaborative deep learning platform based on a blockchain-empowered incentive mechanism, which allows trainers of multiple similar models to cooperate for training deep learning models while protecting confidentiality and auditing correctness of shared parameters. The invention has the following technical effects. Firstly, the encryption method used by model trainers protects the confidentiality of sharing parameters; furthermore, the updated parameters are decrypted through the cooperation of all participants, which reduces the possible disclosure of parameters. Secondly, the encrypted parameters are stored in the blockchain, and are only available to participants and authorized miners who are responsible to update parameters.