Federated learning information processing method and system, storage medium, program and terminal

Abstract

The invention belongs to the technical field of wireless communication networks, and discloses a federated learning information processing method and system, a storage medium, a program, and a terminal. A parameter serve confirms a training task and an initial parameter and initialize a global model. The parameter server randomly selects part of participants to issue model parameters, encrypts themodel parameters and forwards the model parameters through the proxy server; the participants receive part of parameters of the model and cover the local model, and the model is optimized by using local data; the participant calculates a model gradient according to an optimization result, selects a part of the model gradient for uploading, adds noise to the uploading gradient to realize differential privacy, encrypts the uploading gradient and forwards the uploading gradient through the proxy server; the parameter server receives the gradients of all participants, and integrates and updates the global model; and the issuing-training-updating process of the model is repeated until an expected loss function is achieved. According to the invention, data privacy protection is realized; the communication overhead of a parameter server is reduced, and anonymity of participants is realized.

Description

technical field [0001] The invention belongs to the technical field of wireless communication networks, and in particular relates to a federated learning information processing method, system, storage medium, program, and terminal. Background technique [0002] At present, with the rapid development and wide application of the Internet of Things, big data, and 5G network architecture, the massive data and real-time service demands generated by network edge devices have far exceeded the capabilities of traditional cloud computing models, and edge computing extends cloud services to the network Edge, which has the advantages of low latency, small bandwidth and privacy protection. Edge computing makes full use of the computing power of a large number of distributed edge nodes, thereby reducing the computing pressure of the cloud data center, but the management of edge nodes is also more complicated, and some edge nodes with poor security may be maliciously attacked by intruders...

AI Technical Summary

Problems solved by technology

[0008] (3) Most methods provide privacy at the cost of reducing model performance or system efficiency. How to balance privacy and efficiency is a technical problem

[0011] (3) Most methods provide privacy at the cost of reducing model performance or system efficiency. How to balance privacy and efficiency is a technical problem

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