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.

翻译：联邦学习（FL）能够在保护终端设备隐私的前提下，训练人工智能（AI）模型。随着计算任务日益由云、边、端设备协同完成，联邦学习可借助端-边-云协同（EECC）范式，实现设备级规模化扩展与实时接入。尽管分层联邦学习（HFL）支持适用于EECC的多层模型聚合，但现有工作假设所有计算节点采用相同模型结构，导致模型规模受限于最弱的终端设备。针对此问题，我们提出聚合式联邦学习（FedAgg），这是一种全新的EECC赋能联邦学习框架，使端、边、云各层训练出的模型在规模上渐进增大，泛化能力逐步增强。FedAgg基于桥接样本在线蒸馏协议（BSBODP），递归组织所有层级间的计算节点，使每对父子计算节点能够相互传递并蒸馏从生成的桥接样本中提取的知识。这一设计在满足联邦学习隐私约束与EECC灵活性要求的同时，充分挖掘更大规模模型的潜力以提升性能。多种场景下的实验表明，FedAgg相比现有最优方法平均提升4.53%的准确率，并显著加速收敛。