Federated Machine Learning: Concept and Applications

Abstract

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.

References in corpus (4)

• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• CryptoDL: Deep Neural Networks over Encrypted Data
• Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

Cited by in corpus (1)

• Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices