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In this paper, we propose Hierarchical Federated Learning with MomentumAcceleration (HierMo), a three-tier worker-edge-cloud federated learningalgorithm that applies momentum for training acceleration. Momentum iscalculated and aggregated in the three tiers. We provide convergence analysisfor HierMo, showing a convergence rate of O(1/T). In the analysis, we develop anew approach to characterize model aggregation, momentum aggregation, and theirinteractions. Based on this result, {we prove that HierMo achieves a tighterconvergence upper bound compared with HierFAVG without momentum}. We alsopropose HierOPT, which optimizes the aggregation periods (worker-edge andedge-cloud aggregation periods) to minimize the loss given a limited trainingtime.

Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks