Today’s AI still faces two major challenges. One is that in most industries, data exists in the form of isolatedislands. The other is the strengthening of data privacy and security. We propose a possible solution to thesechallenges: secure federated learning. Beyond the federated learning framework first proposed by Google in2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federatedlearning, vertical federated learning and federated transfer learning. We provide definitions, architectures andapplications for the federated learning framework, and provide a comprehensive survey of existing workson this subject. In addition, we propose building data networks among organizations based on federatedmechanisms as an effective solution to allow knowledge to be shared without compromising user privacy. Read More

#federated-learning, #neural-networks, #split-learning