Federal learning method based on network unloading

Abstract

The invention discloses a federated learning method based on network unloading, which is used for improving the performance of the federated learning method while ensuring (improving) the privacy of terminal data. The method comprises the following steps: in a wireless communication system with a plurality of terminals and an edge server, jointly training an artificial intelligence network by using the terminals and the edge server; compared with a traditional federal learning working mode, carrying out the training of partial networks by unloading a terminal to an edge server; in the training process, selecting a terminal which is good in wireless communication network environment and small in calculation and transmission load to participate in training of the network model. According to the invention, the calculation load of the communication terminal is effectively reduced, and the communication load caused by a federated learning method is reduced.

Description

technical field [0001] The invention relates to the field of artificial intelligence and communication, in particular to a federated learning method based on network offloading. Background technique [0002] In recent years, artificial intelligence (AI) technology has developed rapidly and has been widely used, and more complex and cutting-edge artificial intelligence technology has been expected to be applied in many fields, including driverless cars, medical care, finance, etc. Data is the foundation of artificial intelligence technology. However, in most industries, due to issues such as industry competition, privacy security, and complex administrative procedures, data often exists in the form of isolated islands. In reality, it is very difficult to integrate data scattered in various places and institutions. The federated learning technology can effectively deal with the above difficulties and use the data in the form of isolated islands for learning. [0003] The Fed...

AI Technical Summary

Problems solved by technology

[0005] However, there are still many challenges in using federated learning in wireless communication scenarios

First of all, in the traditional federated learning application scenario, the data between the server and the terminal usually uses a limited connection, so the communication overhead is negligible. High requirements, the communication overhead introduced by federated learning will have an impact on network performance, affecting the original transmission rate, delay, reliability and other performance

Secondly, federated learning will generate a large amount of computing overhead on the communication terminal, especially in the scenario of the Internet of Everything, the computing capabilities of various communication terminals are uneven, and local computing will bring a large computing load to the terminal

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