2026· Poster Volume 0008 The 2026 Twenty-Second International Conference on Intelligent Computing July 23-26, 2026 Toronto, Canada· 0 citations
TL;DR
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.
Abstract
Federated Learning (FL) is vulnerable to backdoor attacks, where adversaries can stealthily manipulate the global model. Most existing defense methods are developed under IID assumptions, an assumption that rarely holds in practice. In highly non-IID settings, heterogeneous data distributions across clients make it difficult to distinguish malicious updates from benign ones, particularly when benign clients exhibit atypical patterns due to minority-class data. To address this challenge, existing defenses operate at different levels of granularity. Coarse-grained methods perform client-level filtering, which often mistakenly excludes benign clients under non-IID conditions. Fine-grained methods instead analyze data at the sample level for more precise detection, but typically rely on explicit per-sample gradient analysis, leading to substantial memory and computational overhead. As a result, defending against backdoor attacks in non-IID environments involves a fundamental trade-off between robustness and computational efficiency. To address this challenge, we propose FedRGD, a federated risk-guided dynamic defense framework that enables efficient fine-grained protection. FedRGD maps sample-level risks into structured parameter masking without requiring explicit per-sample gradient storage. It combines feature inconsistency detection with lightweight masking and robust aggregation to achieve both accuracy and efficiency. Extensive experiments on CIFAR-10 and Fashion-MNIST demonstrate that FedRGD consistently reduces the attack success rate while maintaining high main-task accuracy, achieving a favorable security-utility balance with low computational overhead.
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
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.
Federated Learning (FL) is vulnerable to backdoor attacks because of its distributed nature in edge computing scenarios. Existing defense methods show limited efficacy as they overlook the deviations among benign local updates caused by statistical heterogeneity and the stealthiness of backdoor attacks. To tackle these issues, we propose FedDAB, a two-phase method that combines local contrastive regularization with alignment checking, to defend against backdoor attacks. In the first phase, FedDAB introduces a novel model-contrastive term into the local objective to enhance direction and magnitude consistency among benign updates. In the second phase, FedDAB employs an alignment checking strategy to evaluate each local update in terms of overall-direction alignment and parameter-level alignment with historical information, excluding updates that exhibit abnormal alignment patterns from global aggregation. We theoretically prove FedDAB's robustness with a convergence rate of $\mathcal{O}(1/T)$. Extensive experiments show that FedDAB outperforms existing defense methods against backdoor attacks.
Hongliang Zhang, Zhongyuan Yu, Guijuan Wang et al.· arXiv.org· 0 citations
Federated Learning (FL) inherently preserves privacy but remains highly vulnerable to backdoor attacks due to its open participation architecture. Existing defenses face two fundamental limitations: first, screening-based aggregation strategies prove ineffective against advanced cross-round attacks where adversaries progressively poison model parameters through multi-round collaboration; second, mitigation techniques often cause significant accuracy degradation due to the deep entanglement between backdoor and primary task parameters. To address these challenges, we propose Fed-CBE, a novel client-side defense algorithm that eliminates backdoors through three synergistic mechanisms: 1) periodic alternating layer resetting disrupts deep parameters to dismantle cross-round backdoor accumulation; 2) indiscriminate forgetting employs entropy maximization on non-ground-truth classes to decouple backdoor associations without prior trigger knowledge; and 3) knowledge distillation with historical local models restores primary task performance. Extensive evaluations on three benchmark datasets and model architectures demonstrate that Fed-CBE achieves highly competitive robustness, limiting attack success rates to near-zero levels in most settings and keeping them exceptionally low even under high malicious-client ratios without compromising primary task performance, significantly outperforming existing defenses.
Chun-Hai Li, Yun-Hui Shen, Ming Xie et al.· IEEE Transactions on Informa...· 0 citations
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 mitigating backdoor attacks across various attack scenarios.
FedPurify is a framework that performs post-training data-free purification to remove malicious backdoors while preserving task-relevant knowledge in FL, and combines contrastive feature alignment with knowledge-preserving self-distillation to remove backdoor effects while preserving benign task performance.
Baolu Xue, Hanyuan Zheng, Tianxing Man et al.· Proceedings of the 32nd ACM...· 0 citations
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