Federated Learning and Its Classifications

Federated learning (FL) is an approach to machine learning (ML) in which the training data is not managed centrally. In the era of data-driven decision-making, the financial industry faces a unique conundrum: how to leverage the power of ML without compromising the privacy and security of sensitive financial data. FL, an innovative ML paradigm, emerges as a transformative solution to this challenge. This chapter provides an overview of the profound implications and promising applications of FL within the financial sector. Data is retained by data parties that participate in the FL process and is not shared with any other entity. This makes FL an increasingly popular solution for ML tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory, or practical reasons.