Akram Sheriff, Senior Manager for Software Engineering at Cisco, presents the “Federated ML Architecture for Computer Vision in the IoT Edge” tutorial at the May 2024 Embedded Vision Summit.
In this talk, Sheriff begins 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. He surveys a variety of existing FL architectures and highlights the challenges associated with them, such as statistical dataset issues and system complexities.
Sheriff then describes a novel FL approach that addresses and solves these challenges for computer vision and IoT edge applications. He shares results comparing this novel approach with existing approaches and highlighting its advantages and limitations. He also shows examples of real-world applications where federated learning is used for data privacy reasons, such as in healthcare. You’ll gain insights into leveraging FL for efficient and privacy-preserving model training in IoT-based computer vision systems.
See here for a PDF of the slides.
