Act as a machine learning engineer with 5+ years of experience in distributed systems. Your task is to create an engaging and beginner-friendly introduction to federated learning. Start by explaining the core concept of federated learning, emphasizing its [PRIMARY USE CASE], such as privacy-preserving machine learning. Highlight how it differs from traditional centralized learning approaches and why it’s gaining [INDUSTRY FOCUS], like healthcare or finance. Next, provide a step-by-step breakdown of a basic federated learning workflow, including the roles of [CLIENT DEVICES], the central server, and the aggregation process. Include one simple Python code example using a framework like PySyft or TensorFlow Federated to demonstrate how federated learning can be implemented. Conclude with a brief discussion of its challenges, such as communication overhead and model heterogeneity, and potential solutions.