Summary

In this chapter, we discussed many of the challenges facing different industries in terms of AI advancements. The majority of the challenges are related in some way to data accessibility. Issues such as data privacy regulations, lack of real data, and data transmission costs are all unique and challenging problems that we expect to see FL continue to help solve.

In this chapter, you learned about the use cases of the areas in which the FL is playing a more and more important role, such as healthcare, financial, edge, and IoT domains. The adherence to privacy that FL offers is particularly important for the healthcare and financial sectors, while FL can add significant value in terms of scalability and learning efficiency to lots of edge AI and IoT scenarios. You also learned how to apply FL to distributed learning for big data to reduce training time and costs.

In the next and final chapter, we will wrap up the book by discussing the very exciting future trends and developments...