Abstract: ederated learning is an emerging distributed machine learning technology. It does not need to collect data, but can train a common model through the cooperation of all parties, which solves the problems of difficult data collection and data privacy security in traditional machine learning. With the application and development of this technology, the research finds that federated learning may still be subject to various attacks. In order to ensure enough security of federated learning, it is very important to study the attack mode and the corresponding privacy protection technology in federated learning. First of all, it introduces the relevant background and knowledge of federated learning, and then gives a brief introduction to the definition of federated learning, and summarizes the development process and Classification of federated learning, then introduces the three elements of federated learning security, from the perspective of sourcing-based and security-based three elements of the classification of security issues in federated learning, It also summarizes its research progress, and then classifies the privacy protection technology, combined with relevant research and application, specifically reviews the secure multi-party computing homomorphic encryption in federated learning Differential privacy and trusted execution environment are four common privacy protection technologies. Finally, the future research direction of federated learning is prospected.