Parallel Assisted Learning

Xinran Wang, Jiawei Zhang, Mingyi Hong, Yuhong Yang, Jie Ding

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


In the era of big data, a population's multimodal data are often collected and preserved by different business and government entities. These entities often have their local machine learning data, models, and tasks that they cannot share with others. Meanwhile, an entity often needs to seek assistance from others to enhance its learning quality without sharing proprietary information. How can an entity be assisted while it is assisting others? We develop a general method called parallel assisted learning (PAL) that applies to the context where entities perform supervised learning and can collate their data according to a common data identifier. Under the PAL mechanism, a learning entity that receives assistance is obligated to assist others without the need to reveal any entity's local data, model, and learning objective. Consequently, each entity can significantly improve its particular task. The applicability of the proposed approach is demonstrated by data experiments.

Original languageEnglish (US)
Pages (from-to)5848-5858
Number of pages11
JournalIEEE Transactions on Signal Processing
StatePublished - 2022

Bibliographical note

Funding Information:
The work of Jiawei Zhang and Jie Ding was supported by the National Science Foundation under Grant DMS-2134148.

Publisher Copyright:
© 1991-2012 IEEE.


  • Assisted learning
  • cooperative learning
  • decentralization
  • federated learning
  • machine learning


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