"保护数据隐私的联邦学习机制：研究与进展"

Federated learning is a rapidly emerging distributed machine learning model that focuses on privacy protection. As data silos become more prevalent and concerns about privacy continue to grow, traditional centralized machine learning approaches are facing significant hurdles. Recent studies have shown that the gradients of machine learning models can leak sensitive user data, which can be exploited by malicious attackers for illegal gains. Thus, it is essential to implement privacy protection mechanisms to safeguard this sensitive information. This comprehensive review investigates the various privacy protection mechanisms utilized in current federated learning systems. Researchers have categorized these mechanisms into five main types based on the privacy protection techniques employed. By analyzing the research approaches and advancements in different privacy protection mechanisms in federated learning, this study aims to enhance the security of federated learning systems and better protect data privacy. The key findings of this review shed light on the importance of incorporating robust privacy protection mechanisms in federated learning systems. By understanding and implementing these techniques, designers and developers can bolster the security of federated learning systems, ensuring that user data is kept safe and secure. Ultimately, this review serves as a valuable resource for researchers, practitioners, and policymakers in the field of federated learning and privacy protection.