Federated Learning Working Party
Aims
- Broaden knowledge of Privacy Preserving ML methods and their application in insurance industry
- Popularize Federated Learning Machine Learning techniques
- Increase awareness of the industry on how the technology might change the way insurance works
- Understand potential difficulties and risks in adopting FL technology in insurance and attempt to address those issues
- Understand when it is worth to collaborate and when companies should rely on their own data
Members
- Małgorzata Śmietanka (Chair)
- Claudio Giancaterino
- Dylan Liew
- Arshad Khan
- John Ng
- Jonathan Bowden
- Steven Perkins
- Zack Chan
What is Federated Learning?
Federated Learning is a new Machine Learning Model, allowing local machines to build a model together while holding training data on device. This removes the need to store sensitive training data on a central server.