提出可任意层数的联邦学习框架，支持高效通信与高精度训练。
A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees
- 通过嵌套聚合实现任意层级的联邦学习架构
- 在高数据异构下仍保持高准确率，优化后性能显著提升
- 针对通信与计算约束，自动确定最优层内迭代次数
现有分层联邦学习模型普遍仅支持两层聚合，限制了大规模复杂网络中的可扩展性与灵活性。本文提出多层分层联邦学习框架QMLHFL，首次通过嵌套聚合将分层联邦学习推广至任意层数和网络结构，并采用层特定量化方案以满足通信约束。我们建立了QMLHFL的完整收敛性分析，推导出通用收敛条件与速率，揭示了量化参数、分层架构及层内迭代次数等关键因素的影响。此外，我们确定了在满足通信与计算总时延约束下的最优层内迭代次数，以最大化收敛速率。实验表明，即使在高数据异构条件下，QMLHFL仍能保持高学习精度，且经优化后性能显著优于随机参数设置。
原文摘要 · Abstract (English)
Almost all existing hierarchical federated learning (FL) models are limited to two aggregation layers, restricting scalability and flexibility in complex, large-scale networks. In this work, we propose a Multi-Layer Hierarchical Federated Learning framework (QMLHFL), which appears to be the first study that generalizes hierarchical FL to arbitrary numbers of layers and network architectures through nested aggregation, while employing a layer-specific quantization scheme to meet communication constraints. We develop a comprehensive convergence analysis for QMLHFL and derive a general convergence condition and rate that reveal the effects of key factors, including quantization parameters, hierarchical architecture, and intra-layer iteration counts. Furthermore, we determine the optimal number of intra-layer iterations to maximize the convergence rate while meeting a deadline constraint that accounts for both communication and computation times. Our results show that QMLHFL consistently achieves high learning accuracy, even under high data heterogeneity, and delivers notably improved performance when optimized, compared to using randomly selected values.
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