Building performant and robust artificial intelligence (AI)–based applications for dentistry requires large and high-quality data sets, which usually reside in distributed data silos from multiple sources (e.g., different clinical institutes). Collaborative efforts are limited as privacy constraints forbid direct sharing across the borders of these data silos. Federated learning is a scalable and privacy-preserving framework for collaborative training of AI models without data sharing, where instead the knowledge is exchanged in form of wisdom learned from the data. This article aims at introducing the established concept of federated learning together with chances and challenges to foster collaboration on AI-based applications within the dental research community.

为牙科构建高性能且强大的基于人工智能 (AI) 的应用程序需要大量且高质量的数据集，这些数据集通常位于来自多个来源（例如，不同的临床机构）的分布式数据孤岛中。由于隐私限制禁止跨越这些数据孤岛的边界直接共享，协作努力受到限制。联邦学习是一个可扩展且保护隐私的框架，用于在没有数据共享的情况下对 AI 模型进行协作训练，取而代之的是，知识以从数据中学到的智慧的形式进行交换。本文旨在介绍联合学习的既定概念，以及在牙科研究界促进基于人工智能的应用程序合作的机遇和挑战。