Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud servers only perform model averaging. However, in real-life situations such as recommender systems, the cloud server has the ability to store historical and interactive features. In this paper, our proposed Edge-Cloud Collaborative Knowledge Transfer Framework (ECCT) bridges the gap between the edge and cloud, enabling bi-directional knowledge transfer between both, sharing feature embeddings and prediction logits. ECCT consolidates various benefits, including enhancing personalization, enabling model heterogeneity, tolerating training asynchronization, and relieving communication burdens. Extensive experiments on public and industrial datasets demonstrate ECCT's effectiveness and potential for use in academia and industry.

翻译：联邦学习（FL）作为边缘计算的主流范式，能够在保护用户隐私的前提下实现分布式模型训练。现有联邦学习范式假设数据仅驻留在边缘设备，云端服务器仅执行模型参数聚合。然而在实际场景（如推荐系统）中，云端服务器具备存储历史特征与交互特征的能力。本文提出的边缘-云端协同知识迁移框架（ECCT）弥合了边缘与云端之间的鸿沟，通过双向传递特征嵌入与预测逻辑值，实现了两者的知识双向迁移。ECCT整合了多项优势，包括增强个性化建模、支持模型异构性、容忍异步训练、降低通信负载等。在公开数据集与工业数据集上的大量实验表明，ECCT在学术界与工业界均展现出显著的实用价值与应用潜力。