Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 360-365· 0 citations· 34 references
Abstract
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. DualFed combines client-side adaptive behavioral anomaly detection with a server-side Collective Behavioral Correlation (CBC) mechanism. On the client side, an Adaptive Adversarially Robust Statistics (AARS) mechanism integrates trimmed estimation with Exponential Moving Average (EMA) and Exponential Moving Variance (EMV) to robustly quantify abnormal performance degradation. On the server side, CBC aggregates client anomaly reports over a sliding temporal window to detect coordinated attacks. Once an attack is confirmed, DualFed activates a non-punitive self-recovery mechanism. Under non-IID settings, DualFed achieves benign accuracies exceeding 84.0MNIST, 52.0dataset, while reducing malicious attack accuracy to near zero and outperforming recent defense baselines.
This work employs the novel dimensionality reduction technique UMAP and a stringent filtering mechanism to effectively identify and exclude potential malicious participants without relying on traditional noise addition methods and demonstrates that the proposed method maintains high main task accuracy while effectively...
A robust detection method featuring a two layer "norm verification + directional consistency" filtering mechanism combined with the Isolation Forest algorithm to remove malicious outliers, a weighted adaptive robust aggregation that dynamically allocates weights based on client accuracy and consistency, updates the glo...
Mei-Bin Qi· International Conference on...· 0 citations
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
SiftFL is proposed, a scheduling-based robust backdoor detection method that sifts out malicious client models at a fraction of the original cost and preserves and improves the robustness of CGAN-based detection while sharply lowering its server-side cost.
BackDFL is presented, a unified benchmark for systematically evaluating DFL under realistic and adaptive backdoor attacks, and demonstrates that both state-of-the-art Byzantine-robust DFL methods and adapted FL backdoor defenses fail under modest malicious participation rates, especially in heterogeneous settings.
M. Bouchiha, Gregory Blanc, Yu-Fei Han· 0 citations
Target-Oriented Feature Decoupling (TOFD), a unified framework that jointly enables proactive detection and robust optimization against a wide range of poisoning attacks, is proposed and theoretical guarantees for the convergence of TOFD are provided.
Yu-Han Xie, Jing Huang, Chen Lyu· 0 citations
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