{"slug": "provably-communication-efficient-and-privacy-preserving-federated-graph-neural", "title": "Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks", "summary": "Researchers have developed CE-FedGNN, a federated graph neural network framework that enables organizations to collaboratively learn from distributed graph data without sharing raw data or frequent embedding exchanges. The framework reduces communication complexity to O(T³/⁴) while maintaining convergence guarantees, and provides formal privacy protections through metric differential privacy that measures privacy relative to embedding space distances. In tests on anti-money laundering and citation network benchmarks, CE-FedGNN achieved strong performance with substantially lower communication costs and robust privacy-preserving noise.", "body_md": "arXiv:2605.26243v1 Announce Type: new\nAbstract: 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 sharing raw data or per-round embeddings by infrequently exchanging aggregated node representations. To handle cross-client dependency and staleness, we introduce a moving-average estimator that continuously tracks node representations and enables their stable reuse across rounds. To provide formal privacy guarantees for the released representations, we adopt the metric differential privacy (metric-DP) framework, which measures privacy with respect to distances in the learned embedding space rather than worst-case input perturbations. This yields meaningful guarantees at noise levels where standard differential privacy becomes overly conservative. We establish convergence to a stationary point at a rate of $O(1/\\sqrt{T})$ with $O(T^{3/4})$ communication complexity. In addition, we derive $(\\varepsilon,\\delta)$-metric-DP guarantees via R\\'enyi differential privacy composition under a public-cohort threat model. Experiments on synthetic interbank anti-money laundering benchmarks and citation networks demonstrate that CE-FedGNN achieves strong performance while significantly reducing communication and maintaining robustness under privacy-preserving noise.", "url": "https://wpnews.pro/news/provably-communication-efficient-and-privacy-preserving-federated-graph-neural", "canonical_source": "https://arxiv.org/abs/2605.26243", "published_at": "2026-05-27 04:00:00+00:00", "updated_at": "2026-05-27 04:29:55.269410+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research", "ai-ethics"], "entities": ["CE-FedGNN"], "alternates": {"html": "https://wpnews.pro/news/provably-communication-efficient-and-privacy-preserving-federated-graph-neural", "markdown": "https://wpnews.pro/news/provably-communication-efficient-and-privacy-preserving-federated-graph-neural.md", "text": "https://wpnews.pro/news/provably-communication-efficient-and-privacy-preserving-federated-graph-neural.txt", "jsonld": "https://wpnews.pro/news/provably-communication-efficient-and-privacy-preserving-federated-graph-neural.jsonld"}}