Discover how the Internet of Things (IoT) is transforming industries, workplaces, and everyday life in this immersive AWS IoT Workshop. Participants will explore AWS IoT services that enable organizations to harness real-time insights from connected devices and scale intelligent applications from edge to cloud. 

Through expert-led sessions, you’ll gain a practical understanding of tools such as AWS IoT SiteWise for industrial data collection and visualization, and AWS IoT Greengrass for building intelligent edge applications. Hands-on labs with AWS IoT Core will guide you through device connectivity, data management, and integration with other AWS services.

By the end of this workshop, attendees will be equipped with the knowledge and experience to design, deploy, and manage secure, scalable IoT solutions—unlocking innovation and driving efficiency across industries.

Abstract 

Federated AI offers a promising path toward building secure, collaborative, and adaptive systems without the need to centralize sensitive data. In this tutorial, we introduce the Flower framework and showcase its use in scenarios such as anomaly detection, intrusion defense, and scalable federated deployments. Through hands-on examples, participants will gain practical insights into designing resilient Federated AI solutions while also reflecting on open challenges and future research directions for trustworthy and secure machine learning.

Facilitator Bios:
William Lindskog-Münzing: William is a Solutions Engineer at Flower Labs, with industry experience in the heavy-asset industries. His research experience at TU Munich includes federated learning for tabular data, and machine learning application in the automotive industry. He recently led the research and development team at TUM.ai, Germany's leading student AI lab. William also contributed with FedPer baseline during Flower's 2023 Summer of Reproducibility initiative.

Dimitris Stripelis: Dimitris Stripelis is a Research Engineer at Flower Labs, with industry experience at Amazon AWS, and Salesforce. He earned his PhD from the University of Southern California and worked as a Postdoctoral Researcher at USC's Information Sciences Institute (USC-ISI). He is a recipient of the USC Myronis Fellowship (2020) and the A.G. Leventis Foundation Educational Grant (2019–2021). His research focuses on federated and distributed machine learning. He has served as a reviewer for many conferences, including NeurIPS, ICML, AISTATS, AAAI, EMNLP, ECAI, and WISE, as well as journals such as TNNLS, TMI, and TKDE. He co-organized the first Federated Learning Systems (FLSys) workshop at MLSys 2023 and the Federated Learning on the Edge (FLEDGE) Symposium at AAAI 2024.

Amazon SageMaker workshop designed to provide both foundational knowledge and hands-on experience with machine learning on AWS. This session will guide participants through the full ML lifecycle—from building, training, and tuning models, to deploying them at scale for real-time and batch inference.

Through expert-led labs, you will explore powerful built-in algorithms like XGBoost, gain practical skills in hyperparameter tuning, and learn how to leverage SageMaker’s deployment and autoscaling capabilities. Additionally, the workshop introduces SageMaker JumpStart, a tool that accelerates the development and deployment of machine learning applications with pre-built solutions and models.

Whether you are just getting started with ML or looking to sharpen your applied skills, this workshop equips you with the knowledge and confidence to bring machine learning projects from idea to production on AWS.