AI and Federated Learning for Cross-Industry Data Collaboration: Applications in Finance, E-Commerce, and Medical Tech

Abstract

Artificial Intelligence (AI) has revolutionized various industries by enabling data-driven decision-making, automation, and enhanced customer experiences. However, data privacy concerns and regulatory restrictions pose significant challenges in sharing and utilizing cross-industry data. Federated Learning (FL) emerges as a transformative solution that allows multiple stakeholders to collaboratively train machine learning models while preserving data privacy. This paper explores the role of AI and Federated Learning in fostering cross-industry data collaboration, focusing on applications in finance, e-commerce, and medical technology. It examines how FL enhances data security, improves predictive analytics, and drives innovation in these industries. The study also addresses key challenges, such as computational overhead, security vulnerabilities, and integration complexities. Furthermore, real-time and hypothetical data and case studies are presented to demonstrate the impact of FL in these sectors. Graphs and tables illustrate performance improvements, accuracy enhancements, and cost reductions achieved through AI-driven FL implementations. Finally, the paper discusses future directions and the potential of FL in shaping the next era of collaborative AI.