Federated Learning for Business Intelligence
Syed Arshad Ali, Manzoor Ansari, Mansaf Alam, Sandip Rakshit · Auerbach Publications eBooks · 2023
Federated learning (FL) can be a powerful tool for business intelligence (BI), providing significant potential for predictive maintenance in Industry 4.0. It provides the capability to access data securely and efficiently from multiple, distributed sources, while still maintaining the privacy of the data. It also offers the flexibility of using a cloud-based model or an on-premises model, depending on the needs of the organization. This paper presents a comprehensive overview of FL for BI and its application in predictive maintenance in Industry 4.0. A distributed machine learning approach known as FL allows data to be trained without the need for centralized storage of data. We discuss the challenges and potential solutions for FL for predictive maintenance in BI. We present several case studies and examples to demonstrate the effectiveness of FL for predictive maintenance. Furthermore, we provide an overview of the advantages and disadvantages of FL for predictive maintenance in BI. Finally, we conclude by presenting potential future directions for the development and adoption of FL for BI.