Internet of vehicles federal learning hierarchical knowledge security migration method based on gradient memory

Abstract

The invention relates to an Internet of Vehicles federal learning hierarchical knowledge security migration method based on gradient memory, and the method comprises the following steps: 1, carrying out the clustering of a plurality of clients through employing a hierarchical clustering algorithm, obtaining a plurality of independent and identically distributed clusters, and merging a plurality of clients with heterogeneous data into the independent and identically distributed clusters; 2, establishing an Internet of Vehicles federated learning model based on a hierarchical cluster architecture; and step 3, performing knowledge migration among different clusters by adopting a knowledge migration federated learning algorithm based on gradient memory so as to alleviate the problem of disastrous forgetting of knowledge migration in the hierarchical cluster architecture. The method has the advantages that the problem of disastrous forgetting is relieved, and the model convergence speed and the model precision are effectively improved.

Description

technical field

[0001] The invention relates to the technical field of federated learning of the Internet of Vehicles, in particular to a method for safely transferring layered knowledge of the federated learning of the Internet of Vehicles based on gradient memory. Background technique

[0002] With the continuous growth of data volume, the improvement of computing hardware performance and the development of deep neural network, Internet of Vehicles, autonomous driving, etc. have made great progress in recent years, most artificial intelligence solutions are centralized, and users collect them All data transmitted to the central data server or the cloud, but this will bring privacy issues, delay and bandwidth limitations, in contrast, distributed architecture is a more privacy-preserving and efficient choice, federated learning (FL) is a An emerging distributed machine learning model, which allows all parties involved in learning to conduct cooperative training under the co...

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