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Federated learning method, device and system, electronic device, storage medium

A learning method and federated technology, applied in machine learning, computer security devices, electrical and digital data processing, etc., can solve the problem that the optimization result is not the best, and achieve the effect of improving the optimization effect.

Active Publication Date: 2022-03-01
ZTE CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the optimization results obtained by the current federated learning methods are often not optimal.

Method used

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  • Federated learning method, device and system, electronic device, storage medium
  • Federated learning method, device and system, electronic device, storage medium
  • Federated learning method, device and system, electronic device, storage medium

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0157] This example describes two federal learning processes based on the two federal learning systems.

[0158] Such as Figure 7 As shown, two federal learning systems include: EMS, a virtual NCCN, an NCCN, and four NE.

[0159] Among them, the virtual NCCN is set in the EMS, and two NEs are hanging in the virtual NCCN, which are NE1 and NE2; NCCN hung down the EMS, and the NCCN hangs two NEs, which are NE3 and NE4, respectively.

[0160] Among them, EMS includes: business application, first task management module, first global model management module, and weight indicator management module; virtual NCCN includes: second task management module and a second global model management module; NCCN includes: third task management Modules and Third Global Models Management Modules.

[0161] Among them, NE1, NE2, NE3, NE4, NCCN, and virtual NCCN are used to implement the first layer of federal learning process, NCCN, virtual NCCN, and EMS for implementing the second layer of federal lear...

example 2

[0177] This example describes the federal learning process based on the two-layer federal learning system.

[0178] Such as Figure 7 As shown, two federal learning systems include: EMS, a virtual NCCN, an NCCN, and four NE.

[0179] Among them, the virtual NCCN is set in the EMS, and two NEs are hanging in the virtual NCCN, which are NE1 and NE2; NCCN hung down the EMS, and the NCCN hangs two NEs, which are NE3 and NE4, respectively.

[0180] Among them, EMS includes: business application, first task management module, first global model management module, and weight indicator management module; virtual NCCN includes: second task management module and a second global model management module; NCCN includes: third task management Modules and Third Global Models Management Modules.

[0181] Among them, NE1, NE2, NE3, NE4, NCCN, and virtual NCCN are used to implement the first layer of federal learning process, NCCN, virtual NCCN, and EMS for implementing the second layer of federal l...

example 3

[0195] This example describes two federal learning processes based on the two federal learning systems.

[0196] Such as Figure 7 As shown, two federal learning systems include: EMS, a virtual NCCN, an NCCN, and four NE.

[0197] Among them, the virtual NCCN is set in the EMS, and two NEs are hanging in the virtual NCCN, which are NE1 and NE2; NCCN hung down the EMS, and the NCCN hangs two NEs, which are NE3 and NE4, respectively.

[0198] Among them, EMS includes: business application, first task management module, first global model management module, and weight indicator management module; virtual NCCN includes: second task management module and a second global model management module; NCCN includes: third task management Modules and Third Global Models Management Modules.

[0199] Among them, NE1, NE2, NE3, NE4, NCCN, and virtual NCCN are used to implement the first layer of federal learning process, NCCN, virtual NCCN, and EMS for implementing the second layer of federal lear...

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Abstract

The application provides a federated learning method, device and system, electronic equipment, and a computer-readable storage medium. The federated learning method is applied to the i-th layer node, where i is greater than or equal to 2 and less than or equal to (N-1) Integer, (N‑1) is the number of federated learning layers, the method includes: receiving the corresponding first gradient reported by at least one downlink (i‑1)th layer node; according to at least one (i‑1)th layer node The first gradient corresponding to the layer node and the (i-1)th layer weight indicator corresponding to the i-th layer node calculate the updated (i-1)th layer global gradient corresponding to the i-th layer node; where, the (i-1)th layer The layer weight index is a communication index.

Description

Technical field [0001] Embodiments of the present application relate to artificial intelligence, particularly related to federal learning methods, devices and systems, electronic equipment, computer readable storage media. Background technique [0002] In the most mature communication field of data, if intelligent, it is clearly facing such a problem, and the calculation load resource required by intelligent is required to have no more powerful resources in the network existing equipment. It is currently difficult to meet the real-time intelligent requirements of communication intelligent development. [0003] For real-time computing requirements required for the storage network, the main solution is to migrate the computing network device (ie, the center compute node) of the network device from the network device to the edge of the mobile access network, and realize real-time intelligent computing requirements on the edge side. Node (NCCN, NEARCOLLECT Computer Node) with data ac...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/60G06F21/62G06K9/62G06N20/00
CPCG06F21/602G06F21/6245G06N20/00G06F18/214G06N3/084G06N3/098
Inventor 杜永生
Owner ZTE CORP