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.

本研究提出了一种名为 ECCT 的边缘云协作知识传递框架，可在边缘和云之间进行双向知识传输，共享特征嵌入和预测。实验表明，ECCT 在提高个性化，允许模型异构性，容忍异步训练和减轻通信负担等方面具有潜在的学术和行业用途。