Federal neural network model training method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a federated neural network model training method, device and equipment and a storage medium, and relates to the technical field of artificial intelligence andthe technical field of cloud. The method comprises the following steps: inputting sample data into a federated neural network model to process the sample data through a first lower-layer model to obtain a lower-layer model output value; respectively inputting the lower-layer model output value, an interaction layer model parameter generated by the first participant and an encryption model parameter obtained by encrypting the interaction layer model parameter based on an RIAC encryption mode into an interaction layer to obtain an output vector of the interaction layer; inputting the output vector into the upper-layer model to obtain an output value of a federated neural network model; inputting the output value into a preset loss function to obtain a loss result; and performing back propagation processing on the federated neural network model according to the loss result. Through the embodiment of the invention, the calculation complexity can be greatly reduced, the calculation amount is reduced, and the time consumption is reduced.

Description

technical field [0001] The embodiment of the present application relates to the field of Internet technology, and relates to but not limited to a training method, device, equipment and storage medium of a federated neural network model. Background technique [0002] Federated Learning (FL, Federated Learning) can use multi-party data sources to train deep learning models (that is, artificial neural network model ANN) and provide model prediction (model prediction) services without the need for data to go out of the domain. Under the premise of ensuring user privacy and data security, federated learning makes full use of multi-party data cooperation to improve the performance of deep learning models, for example, to improve the accuracy of recommendation models, while ensuring that multi-party data cooperation meets the requirements of data protection laws and regulations . In particular, vertical federated learning can expand data feature dimensions or obtain data label inf...

AI Technical Summary

Problems solved by technology

[0004] However, the encryption and decryption process of the homomorphic encryption scheme based on the Paillier algorithm requires a large number of modular exponentiation and modular inverse operations, and its computational complexity is very high, the amount of calculation is too large, and the time is too long. Vertical federated neural network model training and model inference services

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