ECS-Guard: Online Logit-Geometry Monitoring for Backdoor Defense in Split Learning
Split Learning (SL) enables edge-cloud training by dividing models between clients and the server, but it is vulnerable to backdoor attacks in which malicious clients inject triggers through intermediate activations. Existing defenses in Federated Learning cannot be applied directly due to SL’s sequential training and limited access to data. We propose ECS-Guard (Energy-Centroid-Shape Guard), a lightweight online detection framework that monitors server-side logits using energy distance, centroid shift, and ellipticity analysis to identify abnormal distributions caused by backdoors. Our method detects malicious clients early, blocks their contributions, and preserves model accuracy under both IID and non-IID settings. On CIFAR-10 with VGG16, we evaluate on Jetson Nano devices for both IID and non-IID, showing that ECS-Guard effectively neutralizes pixel-trigger and semantic backdoor attacks while maintaining high accuracy. Specifically, the final Attack Success Rate will drop below 5% in most cases, while preserving the main accuracy relative to vanilla SL. This demonstrates the feasibility of practical and secure Split Learning through insitu monitoring.