论文标题
在多层网络中具有动量加速的分层联合学习
Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks
论文作者
论文摘要
在本文中，我们提出了层次联合学习的动量加速度（HIERMO），这是一种三层工人 - 边缘云联合学习算法，该算法适用于训练加速。在三个层中计算和汇总动量。我们为HIERMO提供收敛分析，显示O（1/T）的收敛速率。在分析中，我们开发了一种新的方法来表征模型聚集，动量聚集及其相互作用。基于此结果，{我们证明HIERMO与Hierfavg相比，与Hierfavg无动量}获得了更紧密的收敛上限}。我们还提出了Hieropt，该hieropt优化了聚合期（工人边缘和边缘云的聚合期），以最大程度地减少给定有限的训练时间的损失。
In this paper, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration. Momentum is calculated and aggregated in the three tiers. We provide convergence analysis for HierMo, showing a convergence rate of O(1/T). In the analysis, we develop a new approach to characterize model aggregation, momentum aggregation, and their interactions. Based on this result, {we prove that HierMo achieves a tighter convergence upper bound compared with HierFAVG without momentum}. We also propose HierOPT, which optimizes the aggregation periods (worker-edge and edge-cloud aggregation periods) to minimize the loss given a limited training time.
