Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity

• URL: http://arxiv.org/abs/2403.01540v1
• Date: Sun, 3 Mar 2024 15:40:24 GMT
• Title: Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity
• Authors: Seyed Mohammad Azimi-Abarghouyi, Viktoria Fodor
• Abstract summary: We present a novel hierarchical federated learning algorithm that incorporates quantization for communication-efficiency. We offer a comprehensive analytical framework to evaluate its optimality gap and convergence rate. Our findings reveal that our algorithm consistently achieves high learning accuracy over a range of parameters.
• Score: 3.8798345704175534
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper presents a novel hierarchical federated learning algorithm within multiple sets that incorporates quantization for communication-efficiency and demonstrates resilience to statistical heterogeneity. Unlike conventional hierarchical federated learning algorithms, our approach combines gradient aggregation in intra-set iterations with model aggregation in inter-set iterations. We offer a comprehensive analytical framework to evaluate its optimality gap and convergence rate, comparing these aspects with those of conventional algorithms. Additionally, we develop a problem formulation to derive optimal system parameters in a closed-form solution. Our findings reveal that our algorithm consistently achieves high learning accuracy over a range of parameters and significantly outperforms other hierarchical algorithms, particularly in scenarios with heterogeneous data distributions.

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