面向分布式机器学习的网络模态创新

doi:10.11959/j.issn.1000-0801.2023128

Abstract

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.

Key words: multimodal network, distributed machine learning, model training, artificial intelligence

CLC Number:

TP393

Zehua GUO, Haowen ZHU, Tongwen XU. Network modal innovation for distributed machine learning[J]. Telecommunications Science, 2023, 39(6): 44-51.

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