Unsupervised Federated Learning on Non-IID Graphs via Contrastive Encoding and Cluster Centroid Sampling

Le résumé fourni par la source

Federated graph learning (FGL) has attracted significant attention for enabling privacy-preserving collaborative model training based on multiple participants’ local graphs. However, a node’s neighbors in one participant’s local graph may be distributed across the local graphs of other participants, resulting in topological nonindependence and creating a specific non-independently and identically distributed (non-IID) problem. Existing unsupervised federated learning approaches under topological nonindependence encounter two problems: 1) the lack of encoding the distances between nodes across different participants’ local graphs reduces the models’ accuracy; and 2) the volume of data transmitted escalates with the increase in inter-edges or nodes, leading to great communication overhead. To tackle these problems, we propose a federated graph learning model based on contrastive encoding and cluster centroid sampling (FCECS), a novel unsupervised FGL model. First, we design a local contrastive strategy based on alignment masking and a global contrastive strategy based on cluster centroid sampling and integrate them to encode node distances both within and across participants’ local graphs, thereby improving the model’s accuracy. Second, the global contrastive strategy based on cluster centroid sampling guarantees a consistent volume of data transmission, thereby reducing communication overhead caused by an increasing number of inter-edges or nodes. Experimental results on real-world datasets demonstrate that FCECS achieves an average accuracy improvement of 57% compared to existing FGL models.

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