Critical infrastructures, such as transportation, healthcare, and energy systems, are becoming increasingly interconnected, creating an urgent need for secure and efficient data sharing between agencies. However, the complexity of inter-agency collaboration is heightened by significant challenges, including privacy concerns, regulatory constraints, and inherent security risks. To address these concerns, Federated Learning (FL), a machine learning technique that facilitates the collaborative training of models across decentralized data sources without the need to transfer sensitive data, has emerged as a highly promising solution. FL ensures that agencies can jointly leverage the power of data-driven insights while ensuring privacy preservation. This paper investigates the potential of federated learning as a means to enable secure, scalable data collaboration between agencies in critical infrastructure sectors. We propose a novel federated learning framework tailored specifically for these sectors, taking into account sector-specific data requirements, regulatory frameworks, and security needs. Additionally, we discuss the effectiveness, challenges, and limitations of the proposed framework, as well as explore its potential for future applications and advancements. This paper aims to contribute to the growing body of research on privacy-preserving machine learning solutions in high-stakes, sensitive environments.