Federated learning is becoming a prominent area of research in computer science, showing significant promise in various applications. Nowadays, we can see innovative methods where millions or even billions of IoT devices can collaborate to achieve a shared machine learning objective through federation. However, current methodologies mainly focus on single-task learning, with one objective and a single task owner, and assume that most devices involved in federated learning share similar designs, types, or limitations. It is believed that federated learning's full potential can only be realized in a dynamic and open ecosystem where a diverse range of devices, industrial units, machine manufacturers, non-governmental organizations, and governmental entities can all contribute to learning across multiple tasks and objectives in a crowdsourced fashion. In this chapter, a multilevel framework is introduced that demonstrates how federated learning, IoT, and crowdsourcing can synergize to create a robust ecosystem for multi-level federated learning tailored for Industry 4.0. This integration supports the development of future intelligent applications for Industry 4.0, such as predictive maintenance and fault detection in smart manufacturing environments. Additionally, various use cases are explored where multi-level federated learning can be applied within Industry 4.0. If successfully implemented, this approach will not only improve performance but also contribute to broader goals like the UN Sustainable Development Goal No. 13, which focuses on reducing the carbon footprint.