Analysis and Prospect of Federated Learning and Privacy Protection Technology

As a new type of distributed machine learning technology, federated learning has shown great application potential in the Internet of things, health care, smart home, finance and other fields. Its core advantage is that it can conduct model training without centralized data, effectively reducing the cost of data transmission and storage, and avoiding the risk of privacy disclosure. However, with the wide application of Federated learning, the problems of data security and privacy protection are increasingly apparent, especially in the face of complex network attacks and data leakage risks. This paper deeply analyzes the basic principles and architecture of Federated learning, and discusses the possible privacy threats in data transmission, model updating and participating devices in detail. Combined with the existing security protection technologies, such as differential privacy, encryption algorithm and secure multi-party computing, this paper discusses how to effectively ensure the security of Federated learning. Finally, the article also looks forward to the future development trend of Federated learning in privacy protection, model optimization, computational efficiency and cross domain collaboration, aiming to provide theoretical support and practical guidance for the further development and application of this technology.