Abstract
This tutorial introduces participants to the practical deployment of Hierarchical Federated Learning (HFL) across the Cloud-Edge-IoT (CEI) continuum. It explains the concept of Federated Learning and its extension to a hierarchical setup, as well as the orchestration aspects of setting up and running HFL pipelines in CEI environments. The tutorial includes a hands-on session which is divided into two parts. In the first part, participants will use the Flower framework to set up and execute standard and hierarchical Federated Learning (FL) tasks, providing hands-on insight into FL without orchestration. The second part focuses on the AIoTwin orchestration middleware, demonstrating how it enables seamless setup and monitoring of HFL pipelines across heterogeneous edge, fog, and cloud environments. The middleware automates the deployment of FL services (clients, aggregators) and handles infrastructure changes affecting the pipeline and global model performance, such as clients joining or leaving a running FL task. Participants are guided through deploying their own custom FL tasks on a real-world distributed cluster, while exploring the middleware’s capabilities for orchestration, resource management, and hierarchical coordination. The tutorial is ideal for researchers and practitioners interested in deploying and running edge-to-cloud FL tasks at scale.
The tutorial was presented as part of the 15th International Conference on the Internet of Things (IoT 2025), held on November 18, 2025, in Vienna, Austria. Website: https://iot-conference.org/ iot2025/workshops-tutorials/.
Keywords: edge computing, Federated Learning, orchestration, MLOps, federated learning
How to Cite:
Čilić, I., Jukić, A., Vuknić, K. & Podnar Žarko, I.,
(2025) “Orchestrating Hierarchical Federated Learning Pipelines with the AIoTwin Middleware”,
IoT Workshop Proceedings 1(1),
61-63.
doi: https://doi.org/10.34749/3061-1008.2025.10
Rights:
Copyright © 2025 The author(s)
