A Framework for Exploring Federated Community Detection
This work addresses the challenge of privacy-preserving graph analysis for domains like social networks, but it is incremental as it explores initial experiments and proposes a framework without major breakthroughs.
The paper tackles the problem of community detection in federated learning settings where graph data is distributed across clients, showing that isolated models can benefit from collaboration, though performance gaps exist due to missing connectivity information.
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.