Title: Designing and Deploying Federated Keyword Spotting Workloads with Flower

Abstract: In this tutorial, the speaker will disscuss how to set up a federated learning pipeline using Flower for the task of keyword spotting. We'll explore how to structure client-server interactions, manage audio preprocessing, and handle imbalance and heterogeneous data across clients. By the end, you’ll have a clear blueprint for building your own scalable and privacy-aware keyword spotting system using Flower and how to run it with both Flower’s Simulation Runtime and its Deployment Runtime.

Bio: Javier is the lead research scientist at Flower Labs. He works on the core framework and develops the Flower Simulation Engine. Javier interests lie in the intersection of Machine Learning and Systems, and more concretely running on-device ML workloads efficiently, a key component in Federated Learning. Javier got his PhD in Computer Science from the University of Oxford in 2021. Before joining Flower Labs, he was a research scientist at Samsung AI (Cambridge, UK)