Federated Machine Learning

Qiang Yang, Yang Liu, Tianjian Chen et al.

2019 · ACM Transactions on Intelligent Systems and Technology · 5,791 citations

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 fed…

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