Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity
<|reference_start|>Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity: 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.<|reference_end|>
@article{azimi-abarghouyi2024quantized,
    title={Quantized Hierarchical Federated Learning: A Robust Approach to
  Statistical Heterogeneity},
    author={Seyed Mohammad Azimi-Abarghouyi, Viktoria Fodor},
    journal={arXiv preprint arXiv:2403.01540},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01540},
    primaryClass={cs.LG cs.IT math.IT}
}