“Federated Machine Learning” (2019), by Qiang Yang et al. Computer Science research paper. Original abstract below, unchanged. No free PDF was found.

Federated Machine Learning

Qiang Yang, Yang Liu, Tianjian Chen et al. · January 28, 2019 · Computer Science

Abstract

Today’s artificial intelligence 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 allowing knowledge to be shared without compromising user privacy.

Source page DOI: 10.1145/3298981