Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2310-2319.DOI: 10.3778/j.issn.1673-9418.2105040

• Artificial Intelligence • Previous Articles     Next Articles

Distributed Model Reuse with Multiple Classifiers

LI Xinchun1,3, ZHAN Dechuan2,3,+()   

  1. 1. Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
    2. School of Artificial Intelligence, Nanjing University, Nanjing 210023, China
    3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
  • Received:2021-05-06 Revised:2021-06-22 Online:2022-10-01 Published:2021-06-24
  • About author:LI Xinchun, born in 1997, M.S. candidate. His research interests include machine learning and data mining.
    ZHAN Dechuan, born in 1982, Ph.D., professor. His research interests include machine learning and data mining.
  • Supported by:
    National Natural Science Foundation of China(61773198);National Natural Science Foundation of China(61632004)


李新春1,3, 詹德川2,3,+()   

  1. 1.南京大学 计算机科学与技术系,南京 210023
    2.南京大学 人工智能学院,南京 210023
    3.南京大学 计算机软件新技术国家重点实验室,南京 210023
  • 通讯作者: + E-mail:
  • 作者简介:李新春(1997—),男,江苏徐州人,硕士研究生,主要研究方向为机器学习、数据挖掘。
  • 基金资助:


Traditional machine learning always takes a data centralized training strategy, while the transmission cost or data privacy protection in many real-world applications results in distributed and isolated data. Distributed learning provides an effective solution for efficient data fusion across isolated islands. However, due to the natural heterogeneity in real-world applications, the distributions of local data are not independently and identically distributed (Non-IID), which poses a huge challenge to distributed learning. First of all, to overcome the problem of data heterogeneity across local clients, this paper introduces model reuse into the procedure of distributed training and proposes a distributed model reuse (DMR) framework. Then, this paper theoretically shows that ensemble learning can provide a universal solution to data heterogeneity, and proposes a technique of multiple classifiers based distributed model reuse (McDMR). Finally, in order to reduce the storage, computation and transmission cost in practical applications, this paper further proposes two specific solutions including multi-head classifier and stochastic classifier based McDMR, which are named as McDMR-MH and McDMR-SC respectively. Experimental results on several public datasets verify the superiorities of the proposed methods.

Key words: learnware, model reuse, multiple classifiers, distributed learning, ensemble, efficiency, privacy protection



关键词: 学件, 模型重用, 多分类器, 分布式学习, 集成, 效率, 隐私保护

CLC Number: