Banking- and healthcare-based AI depends on a lot of data. Maintaining such a huge volume of data on the cloud and running a machine learning model would require lots of resources. But can we do better? Federated learning is an approach to learn in a distributed fashion from billions of data sources, without needing a central repository. It is the next-gen application in banking and healthcare because both the data are restricted by central authorities like banks/health centers. With federated learning, such bodies will be able to share "knowledge" with each other without explicitly sharing data. This is a huge implication for areas like financial crime, where banks can help one another to keep their customers safe without sharing all their confidential data, only the learnings. Health centers can conduct testing of new approaches at mass scale or can learn from diagnoses that work. How can your organization leverage this? This session will help you answer that.