Federated Learning Life Cycle

This section provides a structured overview of the Federated Learning (FL) life cycle, organized around four key elements essential for secure, distributed model development:

• Governance: Policies, compliance, and decision-making frameworks that ensure responsible data and model management throughout the FL process.
• Infrastructure: The technical and organizational setup required to support federated learning, including hardware, software, and network resources.
• Wrangling: The processes of preparing, cleaning, harmonizing, and transforming data to make it suitable for federated analysis.
• Analysis: The methods and tools used to train, evaluate, and interpret federated models, turning distributed data into actionable insights.

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