Keynote Speech 4 Federated Learning for IoT

As the Internet of Things (IoT) continues to proliferate, the need for advanced, privacy-preserving machine learning techniques becomes paramount. Federated learning (FL) offers a decentralized approach that enables edge devices to collaboratively train models without sharing raw data, thus preserving privacy and enhancing security. This tutorial provides an in-depth exploration of federated learning within the IoT context. We will cover the foundational principles of FL, its architecture, and key algorithms. Participants will learn about the unique challenges of applying FL to IoT, including data heterogeneity, limited computational resources, and communication constraints. Practical sessions will include hands-on exercises with the Flower (https://flower.ai) federated learning framework, demonstrating how to implement FL. Attendees will gain insights into the latest research advancements, best practices, and future directions in federated learning for IoT, equipping them with the knowledge to leverage FL in their own IoT applications.