Federated Learning (FL) enables training Artificial Intelligence (AI) models
over end devices without compromising their privacy. As computing tasks are
increasingly performed by a combination of cloud, edge, and end devices, FL can
benefit from this End-Edge-Cloud Collaboration (EECC) paradigm to achieve
collaborative device-scale expansion with real-time access. Although
Hierarchical Federated Learning (HFL) supports multi-tier model aggregation
suitable for EECC, prior works assume the same model structure on all computing
nodes, constraining the model scale by the weakest end devices. To address this
issue, we propose Agglomerative Federated Learning (FedAgg), which is a novel
EECC-empowered FL framework that allows the trained models from end, edge, to
cloud to grow larger in size and stronger in generalization ability. FedAgg
recursively organizes computing nodes among all tiers based on Bridge Sample
Based Online Distillation Protocol (BSBODP), which enables every pair of
parent-child computing nodes to mutually transfer and distill knowledge
extracted from generated bridge samples. This design enhances the performance
by exploiting the potential of larger models, with privacy constraints of FL
and flexibility requirements of EECC both satisfied. Experiments under various
settings demonstrate that FedAgg outperforms state-of-the-art methods by an
average of 4.53\% accuracy gains and remarkable improvements in convergence
rate.

提出了一种新的基于 EECC 的 FL 框架 ——Agglomerative Federated Learning (FedAgg)，它实现了具有实时访问的终端 - 边缘 - 云协同的设备级扩展，通过 Bridge Sample Based Online Distillation Protocol (BSBODP) 来递归地组织计算节点，实现每对父 - 子计算节点之间的知识传递和提取，提高了性能并满足了 FL 的隐私约束和 EECC 的灵活性要求。实验证明，FedAgg 相比最先进的方法，在准确率上平均提高了 4.53％，并在收敛速度上有显著的改进。