Patent ID: 11943114
Assignee: SHANDONG UNIVERSITY
Field: Digital communication (Electrical engineering)
Classification: CPC H  Y | IPC H

Claim 4:
5. The active edge caching method based on community discovery and weighted federated learning according to claim 1, wherein training a content popularity deep learning prediction model, namely, DL model with an attention weighted federated learning framework comprises:
(vi) selecting user terminals to participate in the training process of content popularity deep learning prediction model, and training the content popularity deep learning prediction model with locally recorded user's historical content request data; after the content popularity deep learning prediction model is trained on different user terminals, transmitting the models to a base station for model aggregation; and
(vii) in the process of model aggregation, assigning, by the base station, different weights to the content popularity deep learning prediction models, namely, local models of different user terminals according to user activities and terminal computing capabilities, wherein the weights are computed by formulas (III) and (IV):, a
      
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     (
     III
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wherein, ar+1u represents an activity of the selected user terminal u in the (r+1)th federated training process, |dr-ρ:ru| represents a quantity of requests for different content by the selected terminal u between time windows [r−ρ, r]; qr+1u represents a computing capability of the selected user terminal u in the (r+1)th federated training process, er+1u represents the number of local training times that the computing capability of the selected terminal U may be performed in the (r+1)th federated process, log( ) is logarithmic computation, and max{e} is the maximum number of local training times;
after the weight computation is completed, performing weighted aggregation on different local models to obtain a global content popularity deep learning prediction model, where weighted aggregation formulas are shown as formulas (V) and (VI):, w
      
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     (
     VI
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wherein, wr+1g and br+1g are a weight and a bias parameter of the content popularity deep learning prediction model after aggregation in the base station; wr+1u and br+1u are a weight and a bias parameter of the local model obtained on the selected user terminal U after local training; ar+1u and qr+1u are weights, computed by formulas (III) and (IV), of the local model trained by the user terminal u.