Federated learning is widely considered to be as a privacy-aware learning method because no training data is exchanged directly between clients. Nevertheless, there are threats to privacy in federated learning, and privacy countermeasures have been studied. However, we note that common and unique privacy threats among typical types of federated learning have not been categorized and described in a comprehensive and specific way. In this paper, we describe privacy threats and countermeasures for the typical types of federated learning; horizontal federated learning, vertical federated learning, and transfer federated learning.

翻译：联邦学习被广泛认为是一种隐私感知的学习方法，因为客户端之间不直接交换训练数据。然而，联邦学习中仍存在隐私威胁，且隐私对策已得到研究。但注意到，在典型联邦学习类型中，常见及特有的隐私威胁尚未得到全面且具体的分类与描述。本文针对典型联邦学习类型——横向联邦学习、纵向联邦学习和迁移联邦学习，描述了其隐私威胁与对策。