Date: Wednesday, May 22
Start Time: 2:40 pm
End Time: 3:10 pm
In this talk we will begin by introducing federated learning (FL) for computer vision in IoT edge applications. Federated learning is an approach to machine learning that enables collaborative training of deployed models while maintaining decentralized data. We will survey a variety of existing FL architectures and highlight the challenges associated with them, such as statistical dataset issues and system complexities. We will then describe a novel FL approach that addresses and solves these challenges for computer vision and IoT edge applications. We will share results comparing this novel approach with existing approaches and highlighting its advantages and limitations. We will also show examples of real-world applications where federated learning is used for data privacy reasons, such as in healthcare. Join us to gain insights into leveraging FL for efficient and privacy-preserving model training in IoT-based computer vision systems.
