Secure computation for federated multi-party databases enables federated querying or federated modeling tasks on private data from multiple databases while preserving data privacy. Such a federation is typically a loosely organized group where the participating databases can dropout at will. However, existing multi-party secure computation systems usually employ privacy-preserving computation schemes such as secret sharing, which require the participants to remain online, resulting in poor system availability. Moreover, the existing system can not predict the number of users and the request speed when providing services to the outside. If these systems are deployed on a private cluster or rented virtual machines from a cloud computing platform, it will experience increased latency during sudden bursts of requests and resource wastage when the request workload is low, leading to poor scalability. With the advancement of cloud computing technology, serverless computing has emerged as a new cloud-native deployment paradigm that offers elastic resource scaling. In this work, we design a system architecture and an indirect communication scheme within the serverless computing framework to architect a highly scalable and highly available multi-party database secure computation system. This system can tolerate database node dropouts and automatically scale system resources in response to dynamic request workload. We implement a prototype of the system based on Alibaba Cloud and OceanBase database, conducting comprehensive experiments evaluation. The results show that our system outperforms existing systems in terms of computational cost, system performance, and scalability for tasks such as low-frequency queries and horizontal modeling. It can save up to 78% in computational costs and improve system performance by over 1.6 times. We also analyze the shortcomings of our system for complex queries and vertical modeling tasks.