Assisted learning: A framework for multi-organization learning

Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to accomplish an organization-specific mission. However, to avoid leaking useful and possibly proprietary information, organizations typically enforce stringent security constraints on sharing modeling algorithms and data, which significantly limits collaborations. In this work, we introduce the Assisted Learning framework for organizations to assist each other in supervised learning tasks without revealing any organization’s algorithm, data, or even task. An organization seeks assistance by broadcasting task-specific but nonsensitive statistics and incorporating others’ feedback in one or more iterations to eventually improve its predictive performance. Theoretical and experimental studies, including real-world medical benchmarks, show that Assisted Learning can often achieve near-oracle learning performance as if data and training processes were centralized.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
StatePublished - 2020
Event34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online
Duration: Dec 6 2020Dec 12 2020

Bibliographical note

Funding Information:
X. Xian and J. Ding were funded by the U.S. Army Research Office under grant number W911NF-20-1-0222. The authors thank Hamid Krim, Mingyi Hong, Enmao Diao, and Ming Zhong for helpful discussions.

Publisher Copyright:
© 2020 Neural information processing systems foundation. All rights reserved.


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