A survey on federated learning applications in healthcare, finance, and data privacy/data security

Federated Learning (FL) has emerged as a promising approach for distributed machine learning. FL enables multiple parties to collaboratively train a model without sharing their local data, which addresses privacy concerns associated with traditional centralized machine learning approaches. However, FL faces several challenges, such as the non-IID nature of data, hindering diversity and performance. This paper provides an overview of FL, including its challenges and applications in the healthcare, finance industry, data security and a number of IoT applications. We discuss the state-of-the-art in FL research and explore its potential for future development. FL has been applied in various domains, such as natural language processing, computer vision, healthcare and finance. In this paper, we focus on the application of FL in healthcare, finance, IoT, insurance industry, discussing its potential impact on data security and privacy. We also highlight the challenges and opportunities associated with FL in these topics. The paper also covers methods proposed to address the challenges in FL, including federated averaging, transfer learning, differential privacy, and secure multi-party computation. Overall, this paper provides an insightful overview of FL, its challenges and applications, and its potential for future development.