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Collaborations among multiple organizations, such as financial institutions, medical centers, and retail markets in decentralized settings are crucial to providing improved service and performance. However, the underlying organizations may have little interest in sharing their local data, models, and objective functions. These requirements have created new challenges for multi-organization collaboration. In this work, we propose Gradient Assisted Learning (GAL), a new method for multiple organizations to assist each other in supervised learning tasks without sharing local data, models, and objective functions. In this framework, all participants collaboratively optimize the aggregate of local loss functions, and each participant autonomously builds its own model by iteratively fitting the gradients of the overarching objective function. We also provide asymptotic convergence analysis and practical case studies of GAL. Experimental studies demonstrate that GAL can achieve performance close to centralized learning when all data, models, and objective functions are fully disclosed.
Author Information
Enmao Diao (Duke University)
I am a fourth-year Ph.D. candidate advised by Prof. Vahid Tarokh in Electrical Engineering at Duke University, Durham, North Carolina, USA. I was born in Chengdu, Sichuan, China in 1994. I received the B.S. degree in Computer Science and Electrical Engineering from Georgia Institute of Technology, Georgia, USA, in 2016 and the M.S. degree in Electrical Engineering from Harvard University, Cambridge, USA, in 2018.
Jie Ding (University of Minnesota)
Vahid Tarokh (Duke University)
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