Federated Machine Learning for Decentralized Financial Data Analysis in Cloud Environments

Abstract

Federated Machine Learning (FML) is an unconventional method that performs decentralized analysis of financial data without the need for sensitive data to be uploaded for secure model training that works across distributed platforms. In this paper, we explored the feasibility of applying FML to the cloud for financial institutions, which ultimately satisfies major privacy-preserving and compliance requirements. We discuss the unique challenges brought up by decentralized settings, including issues with data heterogeneity, communication efficiency, and convergence. As a solution, we present a federated learning framework that enables collaborative training under a cloud infrastructure while ensuring that private data does not leave the local institutions. This is to improve performance, maximize the use of resources, increase speed and scalability of analytical actions in the finance sector. The experimental results demonstrate the efficacy of the proposed system in delivering trustworthy and secure financial predictions, paving the way for considerable improvements in decentralized machine learning for the financial industry.