Federated Learning and Deep Learning: A New Frontier for Real-World Applications - Sridhar K. (Science and Technology: Developments and Applications Vol. 3, 2025)
Both federated learning and deep learning are two of the most discussed, maybe biggest technologies in real-world application development across many industries and full of business significance, so in this chapter we will take a look at these interdisciplinary research aspects and explain their relationship. Federated learning provides a decentralized framework where models can be trained collaboratively without having to disclose sensitive data. The combination of federated learning and deep le data in diverse domains like healthcare, finance, smart devices, autonomous vehicles, and retail but much more useful due to the fact that deep learning can automatically extract effective and complex feature representation with a sufficient amount of data. The chapter highlights the importance of these two paradigms in solving critical data privacy issues and promoting innovations by examining how they work together, their benefits and drawbacks. It also offers recommendations for future research and applications, stressing the need for ethics in AI and the spirit of interdisciplinary collaboration. This convergence, therefore, represents another frontier in artificial intelligence that can provide solutions that are more secure, inclusive, and impactful in a world that is increasingly driven by data. Recent research has emerged in the form of federated learning of deep neural networks (DL), which showcases its potential for data privacy in a diverse range of applications, including healthcare, finance, smart devices, autonomous vehicles, and retail.