Federated Learning -- Methods, Applications and beyond

Federated Learning addresses privacy concerns in regulated industries: The paper highlights that fields like medicine with sensitive patient data are heavily regulated, and federated learning provides a framework to train AI models without centralizing personal information—aligning with EU trustworthy AI initiatives and privacy legislation requirements.

Data distribution is a practical barrier to traditional machine learning: Organizations often cannot pool their data for classical training approaches because data exists in distributed forms (across different features or samples), making federated learning a necessary solution for real-world collaborative AI development.

Google's 2016 federated learning framework solved dual challenges: The framework simultaneously tackles two major problems in responsible AI—protecting data privacy while enabling model training on distributed datasets—suggesting that privacy-preserving machine learning is technically feasible at scale.

Multiple federated learning approaches exist for different scenarios: The paper identifies distinct methods including vertical federated learning, horizontal federated learning, and federated transfer learning, indicating that organizations have flexible options to implement privacy-preserving AI depending on how their data is structured.