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 language||English (US)|
|Number of pages||11|
|Journal||IEEE Transactions on Signal Processing|
|State||Published - 2022|
Bibliographical noteFunding Information:
The work of Jiawei Zhang and Jie Ding was supported by the National Science Foundation under Grant DMS-2134148.
© 1991-2012 IEEE.
- Assisted learning
- cooperative learning
- federated learning
- machine learning