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[2505.08145] A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees
Summary
This article presents a novel Multi-Layer Hierarchical Federated Learning framework (QMLHFL) that enhances scalability and flexibility in federated learning by allowing arbitrary aggregation layers and optimizing convergence rates.
Why It Matters
The proposed framework addresses limitations in existing hierarchical federated learning models, enabling more complex and scalable applications in distributed machine learning. By improving learning accuracy under data heterogeneity, it has significant implications for real-world applications in various industries.
Key Takeaways
- QMLHFL generalizes hierarchical federated learning to multiple layers.
- The framework includes a layer-specific quantization scheme to optimize communication.
- A comprehensive convergence analysis reveals key factors affecting performance.
- Optimal intra-layer iterations can maximize convergence rates under constraints.
- QMLHFL shows improved accuracy even with high data heterogeneity.
Computer Science > Machine Learning arXiv:2505.08145 (cs) [Submitted on 13 May 2025 (v1), last revised 15 Feb 2026 (this version, v2)] Title:A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees Authors:Seyed Mohammad Azimi-Abarghouyi, Carlo Fischione View a PDF of the paper titled A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees, by Seyed Mohammad Azimi-Abarghouyi and 1 other authors View PDF Abstract:Almost all existing hierarchical federated learning (FL) models are limited to two aggregation layers, restricting scalability and flexibility in complex, large-scale networks. In this work, we propose a Multi-Layer Hierarchical Federated Learning framework (QMLHFL), which appears to be the first study that generalizes hierarchical FL to arbitrary numbers of layers and network architectures through nested aggregation, while employing a layer-specific quantization scheme to meet communication constraints. We develop a comprehensive convergence analysis for QMLHFL and derive a general convergence condition and rate that reveal the effects of key factors, including quantization parameters, hierarchical architecture, and intra-layer iteration counts. Furthermore, we determine the optimal number of intra-layer iterations to maximize the convergence rate while meeting a deadline constraint that accounts for both communication and computation times. Our results show that QMLHFL consistently achieves high learning accu...
