Quality-aware edge intelligent federated learning method and system

Abstract

The invention discloses a quality-aware edge intelligent federated learning method and a quality-aware edge intelligent federated learning system. The method comprises the following steps that: a cloud platform constructs a federated learning quality optimization problem by taking the maximum sum of aggregation model qualities of a plurality of learning tasks in each iteration as an optimization target and solves the problem: in each iteration, the learning quality of participating nodes is predicted by utilizing historical learning quality records of the participating nodes, and the learningquality of the node training data is quantified by using the reduction amount of a loss function value in each iteration; in each iteration, the cloud platform stimulates nodes with high learning quality to participate in federated learning through a reverse auction mechanism; therefore, the distribution of learning tasks and learning rewards is carried out; in each iteration, for each learning task, each participation node uploads its local model parameters to the cloud platform to aggregate to obtain a global model. According to the method and the system, richer data and more computing powercan be provided for model training under the condition of protecting data privacy so that the quality of the model is improved.

Description

technical field [0001] The invention relates to a performance optimization technology of a large-scale distributed intelligent learning system, in particular to a quality-aware edge intelligent federated learning method and system. Background technique [0002] With the rapid development of the Internet of Things (IoT, Internet of Things), a large amount of data is continuously generated at the edge of the network, which provides opportunities for realizing intelligent services based on machine learning. Traditionally, centralized machine learning frameworks need to aggregate huge training data to cloud centers for model training. Although the centralized machine learning method can achieve satisfactory learning performance, the data transmission and centralized storage will have the risk of privacy leakage, and the data transmission overhead will affect the number of power-constrained mobile devices and the cost of cloud center data maintenance. , are huge obstacles to sys...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a quality-aware edge intelligent federated learning method and system to solve the technical problem that the existing aggregation model ...

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