Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning
This paper identifies network topology as a fundamental privacy vulnerability in federated learning systems, demonstrating that adversaries can infer sensitive data distribution patterns through communication patterns, parameter magnitudes, and structural correlations even under strong differential privacy guarantees.