Computing task acceleration processing method, device and equipment for federated learning

A computing task, federated technology, applied in neural learning methods, computer security devices, transaction processing, etc., can solve the problems of increasing the computational workload of the federated learning process and low efficiency of federated learning.

Active Publication Date: 2020-12-29
CLUSTAR TECH LO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Data expansion and basic operator changes greatly increase the computational workload of the federated learning process, thus making federated learning inefficient

Method used

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  • Computing task acceleration processing method, device and equipment for federated learning
  • Computing task acceleration processing method, device and equipment for federated learning
  • Computing task acceleration processing method, device and equipment for federated learning

Examples

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Embodiment Construction

[0041]The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and realize the subjects described herein, and is not to limit the scope of protection, applicability or examples set forth in the claims. The function and arrangement of the discussed elements can be changed without departing from the scope of protection of the content of this specification. Various examples can omit, substitute, or add various processes or components as needed. For example, the described method may be executed in a different order from the described order, and various steps may be added, omitted, or combined. In addition, features described relative to some examples can also be combined in other examples.

[0042]As used herein, the term "including" and its variations mean open terms, meaning "including but not limited to". The term "based...

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Abstract

The embodiment of the invention provides a method and device for achieving accelerated processing of a computing task. The method comprises the steps: receiving a calculation task and calculation datafrom an external device; determining a parallel computing scheme of the computing task according to the operation type of the computing task and the data size of the computing data, wherein the parallel computing scheme comprises the number of parallel computing blocks and a block thread resource allocation scheme of each parallel computing block, realizing the parallel computing in each parallelcomputing block by utilizing the plurality of distributed threads. calling a parallel computing architecture to execute parallel computing according to the partitioning thread resource allocation scheme to obtain a computing result; and providing the calculation result to the external device. By means of the method, a double-layer parallel processing scheme based on block parallelism and thread parallelism can be used for efficiently achieving computing task parallel processing.

Description

Technical field[0001]The embodiments of this specification generally relate to the field of federated learning, and more particularly to methods, devices, and hardware acceleration devices for implementing accelerated processing of computing tasks in federated learning.Background technique[0002]In application scenarios such as federated learning, computationally intensive task processing will be encountered. For example, in the process of federated learning, each modeling participant iterates the data locally, thereby calculating parameters such as gradients and neural network weights. These parameters will be obtained by the central coordinator. When the central coordinator is not completely trustworthy, the privacy of modeling participants may be leaked. In order to solve the problem of data privacy security, existing federated learning frameworks such as FATE and AngelFL have adopted Paillier homomorphic encryption technology. In a dense state environment, each modeling participa...

Claims

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Application Information

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IPC IPC(8): G06F9/46G06F9/50G06F21/60G06N3/04G06N3/08
CPCG06F9/466G06F9/5027G06F21/602G06N3/08G06N3/045
Inventor 杨林彬胡水海陈天健黄启军陈瑞钦陈振南
Owner CLUSTAR TECH LO LTD
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