Federal learning method based on dynamic adjustment model aggregation weight

A technology of dynamic adjustment and learning methods, applied in computing models, machine learning, computing, etc., can solve problems such as inability to provide universality and good solutions to heterogeneous data, so as to improve the quality of the global model, rationally aggregate weight distribution, and accelerate The effect of convergence speed

Pending Publication Date: 2021-07-13
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that existing federated learning methods cannot provide heterogeneous data solutions with good versatility, the present invention starts with model training information, mines client data and model features without revealing user privacy, and integrates model accuracy, data Quantitati

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  • Federal learning method based on dynamic adjustment model aggregation weight
  • Federal learning method based on dynamic adjustment model aggregation weight

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Embodiment Construction

[0017] It should be understood that all combinations of the foregoing concepts as well as additional concepts described in more detail below may be considered part of the inventive subject matter, provided such concepts are not mutually inconsistent. Additionally, all combinations of claimed subject matter are considered part of the inventive subject matter.

[0018] The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description when taken in conjunction with the accompanying drawings. Other additional aspects of the invention, such as the features and / or advantages of the exemplary embodiments, will be apparent from the description below, or learned by practice of specific embodiments in accordance with the teachings of the invention.

[0019] The federated learning method based on dynamically adjusting model aggregation weights of the present invention is applicable to federated learning includin...

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Abstract

The invention discloses a federal learning method based on a dynamic adjustment model aggregation weight, and the method comprises the steps that: a cloud server receives a local training model and a data quality index from each client, and if a client weight updating condition is reached, a contribution score for each client is calculated according to the contribution of the data quality, the model precision and the model difference index to the model training precision, and weighted average is performed to generate a global model; and the cloud server issues the updated global model to the clients, each client carries out model training on the local training data after receiving the global model, and uploads the local model and the data quality index to the cloud server again after training is finished. According to the method, client training data such as data distribution, model precision and model difference are reasonably utilized, the dynamic aggregation weight is generated, available features in the client training process are fully mined, and a global model with higher quality is formed, so that the model precision and convergence efficiency are improved.

Description

technical field [0001] The invention relates to the field of distributed machine learning, in particular to a federated learning method based on dynamically adjusting model aggregation weights. Background technique [0002] As digital technology enters a period of rapid development, technologies such as big data and artificial intelligence usher in explosive development. On the one hand, this brings new opportunities for upgrading and transformation of traditional business formats; on the other hand, it inevitably brings new challenges to data and network security. a whole new challenge. In order to ensure data security and privacy, various companies are unwilling to share data, which makes the data of each enterprise can only be processed locally, resulting in the problem of data islands, and data islands prevent companies and researchers from repeatedly analyzing and mining data characteristics, hindering The development and application of big data and artificial intellig...

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Application Information

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IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 牟元凯曾艳袁俊峰万健张纪林
Owner HANGZHOU DIANZI UNIV
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