Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity

arXiv:2403.01540v1 Announce Type: new
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 …

abstract aggregation algorithm algorithms arxiv communication cs.it cs.lg efficiency federated learning gradient hierarchical math.it multiple novel paper quantization resilience robust set statistical type