Federated learning is a machine learning approach that allows multiple data owners to collaboratively train a model without sharing their data. This addresses the challenges of data isolation and data privacy in AI. The paper introduces a comprehensive secure federated learning framework, including horizontal, vertical, and federated transfer learning. It discusses the privacy concerns in federated learning, such as secure multi-party computation, differential privacy, and homomorphic encryption. The paper also categorizes federated learning based on data distribution and presents architectures for horizontal and vertical federated learning. It highlights the applications of federated learning in various industries, such as smart retail and healthcare, where data privacy is crucial. The paper also discusses the business model of federated learning, which enables data sharing without data exchange, and the use of blockchain for profit allocation. The paper concludes that federated learning has the potential to overcome the challenges of data isolation and privacy in AI, enabling the development of a secure and collaborative AI ecosystem.Federated learning is a machine learning approach that allows multiple data owners to collaboratively train a model without sharing their data. This addresses the challenges of data isolation and data privacy in AI. The paper introduces a comprehensive secure federated learning framework, including horizontal, vertical, and federated transfer learning. It discusses the privacy concerns in federated learning, such as secure multi-party computation, differential privacy, and homomorphic encryption. The paper also categorizes federated learning based on data distribution and presents architectures for horizontal and vertical federated learning. It highlights the applications of federated learning in various industries, such as smart retail and healthcare, where data privacy is crucial. The paper also discusses the business model of federated learning, which enables data sharing without data exchange, and the use of blockchain for profit allocation. The paper concludes that federated learning has the potential to overcome the challenges of data isolation and privacy in AI, enabling the development of a secure and collaborative AI ecosystem.