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12 April 2021 Assisted learning: cooperative AI with autonomy
Jiaying Zhou, Xun Xian, Na Li, Jie Ding
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Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117461P (2021) https://doi.org/10.1117/12.2586887
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
The rapid development in data collecting devices and computation platforms produces an emerging number of agents, each equipped with a unique data modality over a particular population of subjects. While an agent’s predictive performance may be enhanced by transmitting others’ data to it, this is often unrealistic due to intractable transmission costs and security concerns. In this paper, we propose a method named ASCII for an agent to improve its classification performance through assistance from other agents, without sharing proprietary data and model information. The method is naturally suitable for privacy-aware, transmission-economical, and decentralized learning scenarios. The method is also general as it allows the agents to use arbitrary classifiers such as logistic regression, ensemble tree, and neural network, and they may be heterogeneous among agents. We demonstrate the proposed method with extensive experimental studies.
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Jiaying Zhou, Xun Xian, Na Li, and Jie Ding "Assisted learning: cooperative AI with autonomy", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461P (12 April 2021); https://doi.org/10.1117/12.2586887
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KEYWORDS
Artificial intelligence
Data modeling
Computer security
Neural networks
Performance modeling
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