Collaborative deep learning methods and collaborative deep learning apparatuses

A collaborative deep learning apparatus, comprising:

• a processor;
• and
• a memory for storing instructions executable by the processor;
• wherein the processor is configured to carry out:
• sending an instruction for downloading a global model to a plurality of user terminals, the global model having a set of current parameters, such that each of the plurality of user terminals downloads the global model based on the instruction, and trains the global model based on a set of training data to generate a set of changes on a part of the current parameters;
• receiving the set of changes from each of the plurality of user terminals;
• storing the set of changes from each of the plurality of user terminals;
• recording a hash value of the set of changes from each of the plurality of user terminals into a blockchain;
• obtaining a storage transaction number from the blockchain for the hash value of the set of changes from each of the plurality of user terminals;
• sending the set of changes from each of the plurality of user terminals and the storage transaction number to the plurality of user terminals, such that each of the plurality of user terminals verifies and evaluates received sets of changes and storage transaction numbers based on a set of verification data, to generate a plurality of evaluation values, and stores the plurality of evaluation values into the blockchain, and the blockchain updates an evaluation matrix based on the plurality of evaluation values from each of the plurality of user terminals, and select a set of target user terminals based on the updated evaluation matrix and a preset consensus contract in the blockchain from the plurality of user terminals;
• receiving the set of target user terminals from the blockchain;
• updating the current parameters of the global model based on sets of changes corresponding to the set of target user terminals;
• and
• returning the sending the instruction for downloading the global model to the plurality of user terminals, to update the global model until the global model meets a preset condition.