Jul 2026· IEEE International Conference on Consumer Electronics· pp. 601-606· 0 citations· 20 references
Abstract
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.
FedRGD is a federated risk-guided dynamic defense framework that enables efficient fine-grained protection against backdoor attacks in non-IID environments, and combines feature inconsistency detection with lightweight masking and robust aggregation to achieve both accuracy and efficiency.
Rui-Ying Wang· Poster Volume 0008 The 2026...· 0 citations
Federated Learning (FL) enables collaborative model training while preserving data privacy. However, it remains highly vulnerable to poisoning attacks, particularly coordinated label- flipping attacks. In this paper, we propose DualFed, a dual-side defense framework for detecting coordinated poisoning behaviors in FL....
Hanene Hamdani, Emna Benmohamed, Hela Ltifi· International Conference on...· 0 citations
This work proposes CAEBA (Conditional AutoEncoder Backdoor Attack), a dynamic hidden backdoor framework that uses a conditional autoencoder to generate target-aware and visually stealthy triggers while progressively implanting the backdoor through federated optimization.
This work proposes Z-PEFT, a lightweight meta-classifier that relies exclusively on layer-wise spectral measures for classification, and achieves the best performance while maintaining low and scalable computational cost among weight-space detectors.
Nicola Pitzalis, Donald Shenaj, Giacomo Cignoni et al.· 0 citations
Federated learning is appealing for privacy-sensitive network systems, yet its practical deployment remains hindered by the following three recurring challenges: (1) client drift under non-IID data, (2) vulnerability to corrupted updates, and (3) the communication cost of repeated model exchange. Most existing approach...
Hua Kun, Wei Wang· 2026 International Conferenc...· 0 citations
This work forms this problem as backdoor generalization under training--inference trigger shift and introduces Lilith, a black-box anchor-to-family framework that achieves high family-wise attack success with limited utility degradation and a small trigger generalization gap.
