mlsanityData to 7 Oct 2026

A Framework for Exploring Federated Community Detection

arXiv 2312.09023 ↗First posted 14 Dec 2023cs.LG cs.SI physics.soc-phcited by 0

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Abstract (arXiv)

Federated Learning is machine learning in the context of a network of clients whilst maintaining data residency and/or privacy constraints. Community detection is the unsupervised discovery of clusters of nodes within graph-structured data. The intersection of these two fields uncovers much opportunity, but also challenge. For example, it adds complexity due to missing connectivity information between privately held graphs. In this work, we explore the potential of federated community detection by conducting initial experiments across a range of existing datasets that showcase the gap in performance introduced by the distributed data. We demonstrate that isolated models would benefit from collaboration establishing a framework for investigating challenges within this domain. The intricacies of these research frontiers are discussed alongside proposed solutions to these issues.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 14 Dec 2023.

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References · 16

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4 more
  1. 2021Federated Graph Learning -- A Position Papercited by 34
  2. 2019Towards Federated Graph Learning for Collaborative Financial Crimes Detectioncited by 33
  3. 2021Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learningcited by 10
  4. 2023Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detectioncited by 4

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