Cooperative Model Dissemination Strategy for Hierarchical Clustering Learning in Edge Computing
Long Zhang, Gang Feng, Zheng Qin, Xiaoqian Li, Shuang Qin, Jian Wang · 2024
Hierarchical clustering learning (HCL) extends traditional parameter server-based distributed learning by clustering heterogeneous user equipments (UEs) via cluster nodes (CN s) located at the edge of the network. Currently, most vanilla model dissemination strategies in distributed learning rely on one-to-many transmissions, inevitably consuming excessive precious bandwidth resources. Consequently, communication-efficiency becomes crucial for HCL in resource-constrained edge networks. In this paper, we propose a multistage cooperative model dissemination strategy to sequentially determine the subsets of CNs that can concurrently transmit models during individual scheduling stages, thereby improving communication efficiency in HCL. First, we formulate the strategy design as an optimization problem to minimize the maximum completion time of the slowest straggler in a communication round of HCL. Then, we design an online learning algorithm, called sequential combinatorial multiarmed bandit (SCMAB) to make sequential and combinatorial decisions in individual stages. Numerical results demonstrate the superiority of our proposed strategy over some benchmarks in terms of communication efficiency.