Federated Learning and Preserving Data Privacy

Businesses rely on AI models that transform data into actionable insights. Traditional methods for creating AI models require a lot of data that is collected at some central location. Federated Learning (FL), however, takes a different approach by turning the centralised paradigm on its head and moving models or functions to be executed to where the data is. As a distributed process that does not require a single depository of data and where different parties can train an AI model without having to share the data, FL can be used in situations where data privacy is paramount. This paradigm shift is also creating new opportunities to democratize AI, which has the potential to transform the data economy. Join this month's episode of the Business Intelligence Report with Eric Topham, Co-Founder & Data Science Director at The Data Analysis Bureau, to learn more about how FL works and what opportunities it creates for consumers and enterprises. Viewers will also hear from the experts about the different use cases for federated learning, especially in the context of customer privacy, regulatory compliance, and integrating siloed data. The topics up for discussion will include: - The emergence of FL - FL, the democratization of data and what this means for Big Tech - How FL can be used as a privacy-preserving technology - Business use cases for FL - How FL can be part of your data strategy Speakers - Dr. Pedro Baiz, Royal Society Entrepreneur in Residence at Imperial College London and Head of AI at eXate - Max Robbins, CEO of AI Market - Rajeshwar Bhandaru, Enterprise Data Architect at Suez This episode is part of The Business Intelligence Report original series with Eric Topham, Co-Founder & Data Science Director at The Data Analysis Bureau. We welcome viewer participation and questions during this interactive panel session.