Applications of Federated Learning in AI, IoT, Healthcare, Finance, Banking, and Cross-Domain Learning

Abstract

In the growing field of artificial intelligence, training models often involve collecting large datasets in a centralized way. This raises concerns about data privacy and security. This chapter explores a novel approach called federated learning (FL). It proposes a shift towards decentralized and collaborative training. Instead of centralizing data, federated learning allows individual devices like smartphones, laptops, or private servers to act as training nodes. Using local data, each node helps improve the model without directly sharing sensitive information. FL has demonstrably enhanced many applications. This chapter presents a comprehensive classification and clustering analysis of the ongoing advancements in FL, encompassing its application to a diverse array of technologies and real-world use cases. Specifically, the analysis delves into the integration of FL with various applications and technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), healthcare (patient data analysis for disease prediction and personalized medicine), mobile devices (on-device personalization of AI features), voice assistants (enhanced speech recognition while safeguarding privacy), finance (collaborative fraud detection and personalized recommendations), autonomous vehicles (safer and more efficient driving through decentralized learning), and cross-domain learning (gaining broader insights while respecting data privacy boundaries). By analyzing the theoretical foundations, practical applications, and ongoing advancements of FL in these areas, this chapter illuminates its potential as a cornerstone for decentralized, privacy-preserving AI development.