Network modal innovation for distributed machine learning

Zehua GUO; Haowen ZHU; Tongwen XU

doi:10.11959/j.issn.1000-0801.2023128

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Network modal innovation for distributed machine learning

Topic: Polymorphic Network | 更新时间：2024-06-05

- Network modal innovation for distributed machine learning
- Telecommunications Science Vol. 39, Issue 6, Pages: 44-51(2023)
- 作者机构：
  
  1. 北京理工大学自动化学院，北京 100081
  2. 延安大学物理与电子信息学院，陕西延安 716099
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023128
  CLC： TP393
- Published Online：2023-06，
  
  Published：20 June 2023
- 稿件说明：
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Zehua GUO, Haowen ZHU, Tongwen XU. Network modal innovation for distributed machine learning[J]. Telecommunications science, 2023, 39(6): 44-51.
DOI：

Zehua GUO, Haowen ZHU, Tongwen XU. Network modal innovation for distributed machine learning[J]. Telecommunications science, 2023, 39(6): 44-51. DOI： 10.11959/j.issn.1000-0801.2023128.

摘要

分布式机器学习作为人工智能的主流计算架构，目前仍然存在数据性能传输不高、模型训练速度慢等缺陷，传统的网络模态无法满足分布式机器学习场景的通信语义，继而无法解决这些缺陷以进一步提升模型训练性能。采用多模态网络技术，基于应用特点设计了面向分布式机器学习场景的新型网络模态及其运行逻辑，为多模态网络技术在垂直行业的应用提供了借鉴意义。

Abstract

Distributed machine learning

as a popular computing architecture for artificial intelligence

still faces challenges of slow model training and poor data performance transmission.Traditional network modalities were un able to meet the communication needs of distributed machine learning scenarios

hindering the improvement of model training performance.New network modalities and operation logic for distributed machine learning scenarios using multimodal network technology were proposed.This approach was designed based on application characteristics and provides implications for the use of multimodal network technology in various industries.

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Keywords

references

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