Review: Recent Applications on Federated Learning

In recent years, federated learning (FL), an emerging paradigm in the field of machine learning (ML), has attracted a lot of interest and acceptance. This Chapter offers a thorough overview of the wide range of uses for FL that has been put to good use. FL has demonstrated to be a transformational technique with far-reaching ramifications by enabling decentralized model training while maintaining data privacy and security. FL has been used in the fields of healthcare and wearables to support personalized medicine, enabling early disease identification and customized treatment suggestions while protecting private health information. Similar to how FL helps in the financial sector, fraud detection is aided by FL when fraudulent activities are collectively identified without the need to exchange client transaction data. Additionally, FL broadens the range of applications it can be used for, including smart grids, self-driving cars, natural language processing, privacy-preserving artificial intelligence (AI) in businesses, edge devices, recommendation systems, public health efforts, and education. In these areas, it fosters group intelligence, individualized experiences, and better decision-making - all within a framework that protects privacy. This review chapter provides information on the state of FL applications today while showcasing its adaptability and promise to disrupt a number of industries. While recognizing its many achievements, the chapter also emphasizes the difficulties that must be overcome for FL to continue developing and changing the field of ML and AI, including model aggregation, communication effectiveness, and security.