In recent years, with the development of software and hardware, the cloud database has become an emerging development trend and can reduce database operation and maintenance costs for small and medium-sized enterprises and individual users. Furthermore, the development of cloud databases has brought huge market demands for database tunings. Researchers have proposed many database self-tuning technologies to support automatic optimization of database knobs. To improve tuning efficiency, existing technologies have shifted from focusing solely on the tuning problem itself to focusing on how to reuse historical experience to find the optimal parameter configuration for the current database instance. However, with the development of cloud databases, users have gradually increased their requirements for privacy protection, hoping to avoid privacy leaks while having efficient data access efficiency. Existing methods do not consider protecting the privacy of users' historical tuning experience, which may cause user load characteristics to be perceived, causing economic losses. This article analyzes the characteristics of cloud database tuning tasks in detail, organically combines the server and the user, and proposes a cloud database knob-tuning technology based on federated learning. First, in order to solve the problem of data heterogeneity in federated learning, this paper proposes an experience screening method based on meta-feature matching to eliminate historical experiences with large differences in data distribution in advance to improve the efficiency of federated learning. In order to protect user privacy, this paper organically combines the characteristics of cloud database services and proposes a federated Bayesian optimization algorithm with the node as the training center. We utilize random Fourier features to complete the protection without distorting the tuning experience. The evaluation results in extensive benchmarks present that our method could achieve competitive tuning performance compared with the existing centralized tuning methods.