mlsanityData to 14 Oct 2026

FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy

arXiv 2205.15896 ↗First posted 31 May 2022cs.DC cs.CR cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

Node embedding aims to map nodes in the complex graph into low-dimensional representations. The real-world large-scale graphs and difficulties of labeling motivate wide studies of unsupervised node embedding problems. Nevertheless, previous effort mostly operates in a centralized setting where a complete graph is given. With the growing awareness of data privacy, data holders who are only aware of one vertex and its neighbours demand greater privacy protection. In this paper, we introduce FedWalk, a random-walk-based unsupervised node embedding algorithm that operates in such a node-level visibility graph with raw graph information remaining locally. FedWalk is designed to offer centralized competitive graph representation capability with data privacy protection and great communication efficiency. FedWalk instantiates the prevalent federated paradigm and contains three modules. We first design a hierarchical clustering tree (HCT) constructor to extract the structural feature of each node. A dynamic time warping algorithm seamlessly handles the structural heterogeneity across different nodes. Based on the constructed HCT, we then design a random walk generator, wherein a sequence encoder is designed to preserve privacy and a two-hop neighbor predictor is designed to save communication cost. The generated random walks are then used to update node embedding based on a SkipGram model. Extensive experiments on two large graphs demonstrate that Fed-Walk achieves competitive representativeness as a centralized node embedding algorithm does with only up to 1.8% Micro-F1 score and 4.4% Marco-F1 score loss while reducing about 6.7 times of inter-device communication per walk.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 31 May 2022.

Builds on

18 of 44 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  5. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  7. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  8. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  9. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 124
  10. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  11. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  12. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
6 more
  1. 2017HARP: Hierarchical Representation Learning for Networkscited by 58
  2. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  3. 2020ASFGNN: Automated Separated-Federated Graph Neural Networkcited by 36
  4. 2019Federated Heavy Hitters Discovery with Differential Privacycited by 32
  5. 2017SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Predictioncited by 19
  6. 2021Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregationcited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 1.

9 papers.

  1. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  2. 2023Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devicescited by 6
  3. 2024HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learningcited by 4
  4. 2023FedST: Secure Federated Shapelet Transformation for Time Series Classificationcited by 1
  5. 2025AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Modelcited by 1
  6. 2025LAFA: Agentic LLM-Driven Federated Analytics over Decentralized Data Sourcescited by 1
  7. 2024L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoderscited by 0
  8. 2025Structure-Preference Enabled Graph Embedding Generation under Differential Privacycited by 0
  9. 2026Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Datacited by 0