Difference between revisions of "WEIGHTED AVERAGE FEDERATED LEARNING BASED ON NEURAL NETWORK TRAINING LOSS: abstract simplified (17672533)"

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The abstract describes a method of wireless communication using a user equipment (UE) that involves updating an artificial neural network during a federated learning process. The updates can be in the form of gradients or updated model parameters. The method also involves keeping track of the training loss observed during the training of the neural network at each epoch of the federated learning process. Finally, the method includes transmitting the updates to a federated learning server, which will aggregate the gradients based on the training loss.
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The abstract describes a method of wireless communication where a user equipment (UE) performs updates to an artificial neural network during a federated learning process. These updates can be in the form of gradients or updated model parameters. The UE also keeps track of the training loss observed while training the neural network at that particular epoch of the federated learning process. Finally, the UE transmits these updates to a federated learning server, which is responsible for aggregating the gradients based on the training loss.

Latest revision as of 16:20, 1 October 2023

The abstract describes a method of wireless communication where a user equipment (UE) performs updates to an artificial neural network during a federated learning process. These updates can be in the form of gradients or updated model parameters. The UE also keeps track of the training loss observed while training the neural network at that particular epoch of the federated learning process. Finally, the UE transmits these updates to a federated learning server, which is responsible for aggregating the gradients based on the training loss.

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