MetisFL is a federated learning framework that enables machine learning model training across distributed nodes without centralizing raw data. It provides communication primitives, aggregation strategies, and scheduling mechanisms for coordinating multiple clients in collaborative training sessions. The framework is implemented in C++ for performance-critical components and Python for accessibility, targeting scalable and secure FL workflows.