The Conference for Machine Learning Innovation

Federated Learning: The Next Generation Machine Learning for Regulated Domains – Healthcare and Banking

Session
Join the ML Revolution!
Register until April15:
✓Save up to 310 €
✓ 2-in-1 conference special
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until April15:
✓Save up to 310 €
✓ 2-in-1 conference special
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now

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.

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Behind the Tracks