The article "Federated Machine Learning: Concept and Applications" by Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong discusses the challenges of data isolation and privacy in the context of artificial intelligence (AI) and proposes secure federated learning as a solution. The authors introduce a comprehensive secure federated learning framework that includes horizontal, vertical, and federated transfer learning. They provide definitions, architectures, and applications for this framework and survey existing works. The article also highlights the importance of building data networks among organizations to share knowledge without compromising user privacy. The authors emphasize the need to shift the focus of AI development from improving model performance to investigating methods for data integration that comply with data privacy and security laws. The article covers the concept of federated learning, its privacy considerations, and various architectural approaches for horizontal, vertical, and federated transfer learning. It also discusses the relationship between federated learning and other related concepts such as privacy-preserving machine learning, distributed machine learning, edge computing, and federated database systems. Finally, the article explores potential applications of federated learning in industries like smart retail, finance, and healthcare, and suggests that federated learning can help break down data barriers and establish a community where data and knowledge can be shared safely and fairly.The article "Federated Machine Learning: Concept and Applications" by Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong discusses the challenges of data isolation and privacy in the context of artificial intelligence (AI) and proposes secure federated learning as a solution. The authors introduce a comprehensive secure federated learning framework that includes horizontal, vertical, and federated transfer learning. They provide definitions, architectures, and applications for this framework and survey existing works. The article also highlights the importance of building data networks among organizations to share knowledge without compromising user privacy. The authors emphasize the need to shift the focus of AI development from improving model performance to investigating methods for data integration that comply with data privacy and security laws. The article covers the concept of federated learning, its privacy considerations, and various architectural approaches for horizontal, vertical, and federated transfer learning. It also discusses the relationship between federated learning and other related concepts such as privacy-preserving machine learning, distributed machine learning, edge computing, and federated database systems. Finally, the article explores potential applications of federated learning in industries like smart retail, finance, and healthcare, and suggests that federated learning can help break down data barriers and establish a community where data and knowledge can be shared safely and fairly.