Federated Learning for Decentralized Financial Forecasting

Abstract

The rapid transformation of technology in financial services has greatly highlighted the need for precise and secure financial forecasting models. Nevertheless, the centralized analysis of financial data is being increasingly limited by privacy legislation and the possibility of data infringement. Federated Learning (FL) appears as a groundbreaking concept, allowing for decentralized model training over various data sources without losing the privacy of the data. The paper investigates the implementation of FL in the decentralized financial forecasting while addressing important issues such as data diversity, communication overload, and non-IID financial dataset model optimization. Using the real-world datasets we assess the efficiency of FL frameworks against the existing centralized methods, thus exposing the higher precision, safety, and ability to scale in forecast viability. The results show the promise of FL in changing the process of financial forecasting, issuing solid estimates of future events while protecting sensitive financial information. This study could be seen as an initial step towards a more widespread application of FL in finance which could lead to the promotion of innovations in secure and decentralized analysis of data.