论文标题
辅助学习：多组织学习的框架
Assisted Learning: A Framework for Multi-Organization Learning
论文作者
论文摘要
在越来越多的AI场景中，不同组织或代理商（例如人和机器人，移动单位）之间的合作对于完成特定组织的任务通常是必不可少的。但是，为了避免泄漏有用的和可能的专有信息，组织通常会对共享建模算法和数据实施严格的安全性约束，这显着限制了协作。在这项工作中，我们为组织介绍了辅助学习框架，以相互协助监督学习任务，而无需透露任何组织的算法，数据甚至任务。一个组织通过广播特定于任务但非敏感的统计数据来寻求帮助，并将他人的反馈纳入一个或多个迭代中，以最终提高其预测性能。理论和实验研究（包括现实世界的医学基准）表明，辅助学习通常可以实现近门会学习绩效，就好像数据和培训过程是集中的一样。
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.
