  Today's AI still faces two major challenges. One is that in most industries,
data exists in the form of isolated islands. The other is the strengthening of
data privacy and security. We propose a possible solution to these challenges:
secure federated learning. Beyond the federated learning framework first
proposed by Google in 2016, we introduce a comprehensive secure federated
learning framework, which includes horizontal federated learning, vertical
federated learning and federated transfer learning. We provide definitions,
architectures and applications for the federated learning framework, and
provide a comprehensive survey of existing works on this subject. In addition,
we propose building data networks among organizations based on federated
mechanisms as an effective solution to allow knowledge to be shared without
compromising user privacy.
