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Federated Learning in Production: Enabling AI Training with Data Privacy

Offered By: Toronto Machine Learning Series (TMLS) via YouTube

Tags

Federated Learning Courses Cloud Computing Courses Regulatory Compliance Courses Edge Computing Courses Data Privacy Courses Distributed Machine Learning Courses

Course Description

Overview

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Explore federated learning and its practical applications in this 25-minute conference talk from the Toronto Machine Learning Series (TMLS). Dr. Maria Börner, Program Manager at Adap GmbH, delves into the advantages of federated learning over traditional cloud-based AI training methods. Discover how this innovative approach enables AI to run and train locally on edge devices, cars, servers, and across different organizations while maintaining data privacy. Learn about the regulatory constraints and latency issues associated with centralized data training, and how federated learning addresses these challenges. Gain insights into various applications and use cases that benefit from federated learning and analytics. Understand the key requirements for implementing federated learning in your projects, and how it allows for sharing learnings without compromising data security.

Syllabus

Federated Learning in Production


Taught by

Toronto Machine Learning Series (TMLS)

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