The AIoTwin project has released a new article in its ongoing blog series, focusing on Hierarchical Federated Learning (HFL) - an emerging approach designed to enhance the scalability and efficiency of distributed learning across Cloud-Edge-IoT (CEI) environments.
While traditional Federated Learning (FL) enables collaborative model training without sharing raw data, it often faces limitations in scalability and communication efficiency. HFL addresses these challenges by introducing an intermediate layer of edge aggregators, creating a robust, multi-tier learning architecture that supports more decentralized and resilient AI applications.
The blog post, authored by Katarina Vuknić following her research exchange at the Distributed Systems Group (DSG), TU Wien, as part of the AIoTwin project, explores several key aspects of HFL:
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Communication Efficiency: How HFL significantly reduces uplink communication costs compared to flat FL architectures.
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Trade-offs: Analyzing challenges such as convergence delay, gradient variance, and model divergence in multi-level aggregation setups.
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Real-World Impact: Demonstrating the use of HFL in applications like smart farming and intelligent traffic management systems.
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The AIoTwin Solution: Presenting the open-source Extension of the Flower Framework for HFL, a Python-based component that enables scalable client, local, and global aggregation across the CEI continuum.
The AIoTwin HFL solution offers a flexible, modular implementation of federated learning services designed to support distributed AI orchestration from the edge to the cloud. It represents a key step forward in advancing open, adaptive, and privacy-preserving machine learning for next-generation intelligent systems.
The full article is available here.