mlsanityData to 7 Oct 2026

Edge-cloud Collaborative Learning with Federated and Centralized Features

arXiv 2304.05871 ↗First posted 12 Apr 2023cs.LG cs.IRcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 12 Apr 2023.

Builds on

20 of 30 references.

References · 20

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  8. 2018AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkscited by 319
  9. 2021A Field Guide to Federated Optimizationcited by 312
  10. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  11. 2021FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusioncited by 49
  12. 2021Device-Cloud Collaborative Learning for Recommendationcited by 34
8 more
  1. 2021FedGraph: Federated Graph Learning with Intelligent Samplingcited by 34
  2. 2020EdgeRec: Recommender System on Edge in Mobile Taobaocited by 29
  3. 2022FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Servercited by 29
  4. 2022Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matchingcited by 24
  5. 2022Intelligent Request Strategy Design in Recommender Systemcited by 10
  6. 2022Device-Cloud Collaborative Recommendation via Meta Controllercited by 10
  7. 2022CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learningcited by 6
  8. 2017DeepConfig: Automating Data Center Network Topologies Management with Machine Learningcited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 3.

5 papers.

  1. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  2. 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
  3. 2025Towards Principled Learning for Re-ranking in Recommender Systemscited by 1
  4. 2024FedGuCci: Making Local Models More Connected in Landscape for Federated Learningcited by 0
  5. 2025Generalizable Federated Learning using Client Adaptive Focal Modulationcited by 0