Multi-task federal learning method and device for edge device

Abstract

The invention relates to a multi-task federal learning method and device for edge devices, and belongs to the technical field of computers. The method comprises the following steps: after at least two learning tasks are created, sending a resource query request to multiple edge devices; determining a resource scheduling strategy of the plurality of edge devices according to resource information queried by the resource query request and a Bayesian optimization algorithm; distributing learning tasks to the plurality of edge devices according to a resource scheduling strategy; for the global model corresponding to each learning task, obtaining model parameters uploaded by each edge device corresponding to the learning task; determining final model parameters of the global model based on the model parameters. The problem that when multiple learning tasks exist, equipment resources cannot be reasonably scheduled, and consequently federal learning efficiency is not high can be solved. By minimizing the sum of the completion durations of the at least two submitted learning tasks, even if a plurality of learning tasks can be converged as quickly as possible, the multi-task learning efficiency can be improved.

Description

【Technical field】 [0001] The present application relates to a multi-task federated learning method and device oriented to edge devices, belonging to the field of computer technology. 【Background technique】 [0002] Federated Learning (Federated Learning) is a machine learning framework. Its design goal is to ensure information security during big data exchange, protect terminal data and personal data privacy, and ensure compliance with laws and regulations. Efficient machine learning between nodes. Among them, the machine learning algorithms that can be used in federated learning are not limited to neural networks, but also include important algorithms such as random forests. [0003] When there are multiple machine learning tasks in the federated learning process, if only one learning task can be run at the same time, the tasks cannot be parallelized, which will increase the waiting time of the tasks and be extremely inefficient. Therefore, when running multiple machine l...

AI Technical Summary

Problems solved by technology

[0004] This application provides a multi-task federated learning method and device for edge devices, which can solve the problem that when there are multiple learning tasks, device resources cannot be reasonably scheduled, resulting in low efficiency of federated learning

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