Kavli Affiliate: Yi Zhou
| First 5 Authors: Zexi Li, Qunwei Li, Yi Zhou, Wenliang Zhong, Guannan Zhang
| Summary:
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.
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