The development of AI application on the edge devices require integrated data, algorithms, and tools. Big companies like Google and Apple have integrated data, algorithms, and tools for building end to end systems with optimized and dedicated hardware for deep learning applications. The Bonseyes [2] EU H2020 collaborative project is an open and expandable AI platform. Bonsey's provides access to advanced tools and services that can be obtained from the data market place and eco-system of collaborative leading academic and industrial partners for adding AI to embedded products. Bonseyes AI Marketplace proposed an end to end AI pipeline to overcome these requirements for the development and deployment of AI solutions on edge devices. In Bonseyes, a Secure virtual premise (SVP) is a secure and protected area for the collaborative and systematic development of AI using Machine Learning AI Pipelines. The centralized SVP has limitations like scalability, reliability, and load balancing. This thesis work focuses on the enhancement of SVP and its mechanisms to adopt a distributed and federated architecture for better performance and fast development of AI applications. It investigates various federation mechanisms and implements a distributed and federated SVP. A Marketplace rendezvous host is designed and implemented from where multiple distributed compute resources can be started, controlled, and stopped by a user. Users and distributed locations related data are stored in an SQLite relational database. Communication between entities of distributed and federated SVP is enabled using HTTPs protocol and PKI Infrastructure is used for authentication and authorization of users. We evaluate the performance of our implementation by calculating the start-up time of multiple resources until a user can perform AI Engineering. The results show that time is directly proportional to the number of nodes started.