Digital teaching platforms (DTPs) are aimed to support personalization of classroom education to help optimize the learning process. A trend for research and development exists regarding methods to analyze multimodal data, aiming to infer how students interact with delivered content and understanding student behavior, academic performance, and the way teachers react to student engagement. Existing DTPs can deliver several types of insights, some of which teachers can use to adjust learning activities in real-time. These technologies require a computing infrastructure capable of collecting and analyzing large volumes of data, and for this, cloud computing is an ideal candidate solution. Nonetheless, preliminary field tests with DTPs demonstrate that applying fully remote services is prohibitive in scenarios with limited bandwidth and a constrained communication infrastructure. Therefore, we propose an architecture for DTPs and an algorithm to promote the adjustable balance between local and federated cloud resources. The solution works by deciding where tasks should be executed, based on resource availability and the quality of insights they may provide to teachers during learning sessions. In this work, we detail the system architecture, describe a proof-of-concept, and discuss the viability of the proposed approach for practical scenarios.