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A multi-institution collaborative learning system and method based on hierarchical parameter server

A learning system and server technology, applied in the field of multi-institution collaborative learning systems based on hierarchical parameter servers, can solve problems such as low resource utilization, high communication costs, and big data data islands.

Active Publication Date: 2020-11-03
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] Aiming at the above-mentioned deficiencies in the prior art, a multi-institution collaborative learning system and method based on a layered parameter server provided by the present invention solves the data island problem of big data, solves the data privacy security problem during multi-party collaboration, and solves the problem of High communication costs, high maintenance costs, high security risks, and low resource utilization of existing systems

Method used

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  • A multi-institution collaborative learning system and method based on hierarchical parameter server
  • A multi-institution collaborative learning system and method based on hierarchical parameter server
  • A multi-institution collaborative learning system and method based on hierarchical parameter server

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Embodiment

[0081] In the cross-domain multi-center scenario, due to the characteristics of high bandwidth and low latency, homogeneous computing and communication resources, security and reliability within the domain, and the characteristics of low bandwidth and high latency, heterogeneous computing and communication resources, and insecurity and reliability between domains , Isolating intra-domain and inter-domain can maximize intra-domain resource utilization, minimize inter-domain communication pressure, and provide flexibility for organizations to choose a suitable communication topology according to their own computing cluster environment. The present invention proposes a multi-party collaborative learning framework HiPS based on a layered parameter server, and isolates intra-domain and inter-domain data interactions through the layered parameter server. The intra-domain fused model update is sent to the central organization by the intra-domain parameter server, and the global model ...

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Abstract

The invention discloses a multi-organization collaborative learning system based on a layered parameter server, which includes a central agency and several participating agencies connected to the central agency through a WAN network. Based on the above system, the present invention also discloses a multi-institution collaborative learning method based on layered parameter servers. The invention solves the data island problem of big data, solves the data privacy security problem during multi-party collaboration, and solves the problems of high communication cost, high maintenance cost, high security risk and low resource utilization rate of the existing system. The present invention realizes multi-party collaborative learning with high communication efficiency and high calculation efficiency under the premise of ensuring data privacy and security, and is suitable for cross-domain interconnection of multiple independent institutions and multiple data centers. The system proposed by the present invention supports a platform mode and a participation mode, which can be used as a platform to provide multi-party knowledge fusion services, and can also be used as a tool to support sharing and collaboration among multiple independent institutions.

Description

technical field [0001] The invention belongs to the field of electronic technology, and in particular relates to a multi-institution collaborative learning system and method based on a layered parameter server. Background technique [0002] In the 5G era of high-speed interconnection of everything, the speed of data collection and the amount of data accumulation have exploded, marking that human society has truly entered the era of big data. Big data puts forward higher requirements for data mining capabilities, and the rapid development of artificial intelligence provides powerful data mining and analysis capabilities for many frontier scientific fields, enabling intelligent applications to extract core knowledge from huge amounts of data, and Organically combine these knowledge to perform complex tasks such as detection, recognition, prediction, decision-making, generation, etc., such as Alipay's face recognition, China Customs' face detection, Douyin short video human bod...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/24H04L29/06H04L29/08
CPCH04L41/145H04L41/0826H04L41/0893H04L63/20H04L67/51
Inventor 虞红芳李宗航李晴孙罡周华漫
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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