Application Domains of Federated Learning
S. Annamalai, N Sangeetha, Miles Kumaresan, Dommaraju Tejavarma, Gandhodi Harsha Vardhan, A. Suresh Kumar · 2024
In machine learning, Federated Learning (FL) is a disruptive technique which makes it possible for decentralized model training across devices or institutions without demanding the transfer of raw data. This chapter offers a comprehensive overview of Federated Learning, beginning with its conceptual framework and progressing to its importance and benefits in contemporary data-driven environments. FL tackles key problems in data security and compliance by strengthening privacy, lowering latency, and lowering data transfer costs while preserving localized data. The chapter addresses a variety of critical application domains where Federated Learning has proven the ability to have significant influence. Without violating patient secrecy, FL promotes cooperative research and tailored healthcare in the healthcare industry. Through safe and effective exchange of information, it aids fraud detection and risk management in banking and finance. FL supports the Internet of Things (IoT) by offering more intelligent, adaptable networks without a need for centralized data collection. FL is used by recommender systems and e-commerce platforms to offer customized user experiences while preserving information about consumers. Decentralized learning models assist telecommunications companies enhance customer service and network optimization. Furthermore, FL is utilized by autonomous car systems for interpreting data in real-time and update models, making self-driving technology more secure and reliable. The aim of this chapter is to provide readers a thorough understanding of federated learning, showcasing its potential to transform a number of industries while also summarizing its various applications along with significant advantages.