Federal learning method with high communication efficiency in wireless communication scene

Abstract

The invention discloses a federated learning method with high communication efficiency in a wireless communication scene. The method comprises the following steps: S1, constructing a federated learning framework from three aspects of a federated learning system, a training algorithm and a communication model in the wireless communication scene; s2, aiming at the constructed federal learning framework, carrying out convergence analysis on the training process of the federal learning framework; and S3, constructing an optimization problem about the federated learning framework according to a convergence analysis result, and solving the problem through a joint optimization method for equipment selection and beam forming. According to the method, on the basis of air calculation and a second-order training algorithm, on one hand, low-delay model aggregation is achieved through the waveform superposition characteristic of a channel, on the other hand, the number of iteration rounds needed by training is reduced through the rapid convergence characteristic of the second-order algorithm, and the communication bottleneck problem existing in most wireless federated learning methods at present is solved. Meanwhile, the training accuracy is further improved through the provided joint optimization method for the federated learning framework.

Description

technical field [0001] The present invention relates to the field of wireless communication and federated learning, in particular to a federated learning method with high communication efficiency in a wireless communication scenario. Background technique [0002] Today, artificial intelligence (AI) related technologies are in the stage of rapid development, and are being researched and applied in various scenarios. As a data-driven technology, its reliability and accuracy largely depend on the size and quality of the data source. However, obtaining a high-quality dataset for AI model training is not easy for most enterprises. At the same time, the issue of data privacy has gradually been paid more and more attention by people, and data in real applications has also formed data islands, which makes it more difficult to aggregate data in the cloud for AI model training. Therefore, federated learning has emerged as a new learning paradigm to solve these problems. A general f...

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Problems solved by technology

[0005] In view of the communication efficiency problems existing in the existing methods, the purpose of the present invention is to provide a federated learning method with high communication efficiency in wireless communication scenarios, and to solve the bottleneck problem of communication efficiency

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