Equipment evaluation and federated learning importance aggregation method, system and equipment based on edge intelligence and readable storage medium

Abstract

The invention provides an equipment evaluation and federated learning importance aggregation method based on edge intelligence, which comprises the following steps of cloud server initialization: generating an initial model by a cloud server, equipment evaluation and selection: receiving resource information of terminal equipment by an edge server, generating a resource feature vector, and inputting the resource feature vector to the evaluation model, local training: after the edge server selects the intelligent equipment, sending the transferred initial model to the intelligent equipment, andenabling the intelligent equipment to carry out local training on the initial model in federated learning to obtain a local model, local model screening: sending the local model to an edge server, and judging whether the local model is an abnormal model or not by comparing the loss values of the local model and a previous round of global model, and global aggregation: performing global aggregation by using a classical federated average algorithm. According to the method provided by the invention, on one hand, the training bottleneck problem with resource constraint equipment is solved, and onthe other hand, the model aggregation effect is improved so as to reduce redundant training and communication consumption.

Description

technical field [0001] The present invention relates to the field of federated learning efficiency, in particular to an edge intelligence-based device evaluation and federated learning importance aggregation method, system, device and readable storage medium. Background technique [0002] With the gradual maturity of deep learning technology, the development of artificial intelligence applications has brought many conveniences to our lives, such as facial recognition and natural language processing. In order for these AI applications to achieve accurate inferences in real life, the models must be learned using large training datasets. These data often need to be collected from the user layer through sensors and stored in cloud servers. With the popularity of the Internet of Things, users have more and more terminal devices. Collecting and transmitting large amounts of data will bring huge communication costs to traditional cloud-based training methods, and the possibility ...

AI Technical Summary

Problems solved by technology

On the one hand, our method solves the training bottleneck problem with resource-constrained devices, and on the other hand, it improves the effect of model aggregation to reduce redundant training and communication consumption.

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