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.