arXiv:2605.26243v1 Announce Type: new Abstract: Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot share raw data due to privacy and policy constraints. Existing federated GNN methods either ignore cross-client links, leading to degraded accuracy, or require frequent embedding exchanges, incurring substantial communication and privacy costs. We propose CE-FedGNN, a communication-efficient and privacy-preserving federated GNN framework for learning over such coupled graphs. Our approach avoids
The increasing pressure to utilize distributed data for AI while respecting stringent privacy regulations and policy constraints drives the need for advanced federated learning solutions.
This development allows for enhanced AI model training on sensitive relational data across organizations without compromising privacy, critical for verticals like healthcare, finance, and cross-border collaborations.
The ability to train performant GNNs on distributed, private data sources without frequent, costly embedding exchanges or ignoring critical cross-client links improves accuracy and practicality of federated AI.
- · Healthcare providers
- · Financial institutions
- · AI/ML researchers
- · Organizations with distributed data
- · Centralized data platforms that rely on raw data sharing
- · Legacy data privacy solutions
Increased adoption of federated learning for complex relational datasets.
Faster development and deployment of secure, privacy-preserving AI applications in regulated industries.
Enhanced global collaboration on AI research and application development without data sovereignty conflicts.
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Read at arXiv cs.LG