Federated Learning and Deep Learning: A New Frontier for Real-World Applications
0Federated Learning and Deep Learning: A New Frontier for Real-World Applications
Layman Abstract: This chapter explores two major technologies—federated learning and deep learning—and how they work together to shape the future of artificial intelligence (AI). Federated learning is a method that allows different devices or organizations to train a shared AI model without ever sharing sensitive data. This makes it especially useful for industries like healthcare and finance, where privacy is crucial.
When combined with deep learning—which is great at finding patterns in large amounts of data—federated learning becomes even more powerful. Deep learning can automatically learn complex features, and when applied in a federated way, it ensures privacy while still producing strong results.
The chapter discusses how this combination is being used in smart devices, self-driving cars, online retail, and more. It also looks at both the strengths and challenges of this approach and offers suggestions for future research, emphasizing the need for ethical practices and teamwork across different fields.
In short, the merging of federated learning and deep learning is creating a new, more secure and privacy-conscious path forward for AI in our data-driven world.
View Book: https://doi.org/10.9734/bpi/stda/v3/3199
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