Sep 2026· IEEE Internet of Things Journal· Vol 13, pp. 40902-40917· 0 citations· 52 references
Abstract
The rapid adoption of Internet of Things (IoT) devices has accelerated the need for privacy-preserving machine learning techniques, such as federated learning (FL). However, the decentralized and collaborative nature of FL makes it vulnerable to backdoor attacks, where adversaries locally update their malicious models before contributing to the global aggregation, subtly injecting backdoors without degrading the normal performance. An affected model behaves as expected during regular operations but exhibits malicious behavior when an embedded trigger is presented. In this article, we propose a novel self-supervised contrastive-learning-based approach to detect and mitigate backdoor attacks in FL within IoT environments. Unlike conventional reverse-engineering methods that iterate through each class in the dataset to reconstruct triggers, our approach directly regenerates triggers from compromised global models without class iteration. This is achieved by comparing last-layer feature representations of a potentially compromised model with those of a relatively clean model under the guidance of contrastive loss. The reverse-engineered trigger is then leveraged to patch the global model and remove the backdoors. We evaluate our method on three benchmark datasets under two federated backdoor attack scenarios, simulating IoT device collaborations. Extended experiments are also conducted on a transformer-based model and two mitigation methods to assess the robustness of our approach. Our results demonstrate that while traditional reverse-engineering techniques are effective in centralized settings, they struggle to detect backdoors in FL. Comparatively, our method is resilient against backdoor attacks across various settings. In addition, our method is more time-efficient because of its capability of generating the backdoor trigger directly without iterating through all classes.
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.
An underexplored threat in which a dishonest orchestrator intentionally manipulates the aggregation process to induce targeted overfitting in local models of specific clients is studied, demonstrating that client-side integrity testing can provide early, effective, and scalable detection, supporting safer deployment of FL systems.
Soumia Zohra El Mestari, M. Zuziak, Gabriele Lenzini· Proceedings on Privacy Enhan...· 0 citations
Federated learning (FL) has been widely applied in Internet of Things (IoT) systems. However, its inherently distributed nature makes it vulnerable to backdoor attacks. Existing attacks typically design triggers based on fixed patterns or local models, without considering their applicability to the global model. Consequently, a significant “loss” occurs during the transfer of the backdoor from the local model to the global model, leading to a low Attack Success Rate (ASR). To address this issue, we propose a novel backdoor attack method, termed Federated Generative Adversarial Trigger (FedGAT). The proposed method optimizes the trigger by training it with publicly available global models and a small number of samples. It adopts a Generative Adversarial Network (GAN) framework, in which the discriminator is fixed as the converged global model to guide the generator’s training through feedback. As a result, the generator can automatically produce optimized triggers that are highly correlated with the global model’s feature space, effectively reducing the “loss” in backdoor transfer and improving attack performance. Extensive experiments on multiple datasets demonstrate that, without amplifying malicious updates, the proposed method achieves an ASR improvement of 60.95–80.19% over the average baseline performance on the global model, exhibiting outstanding attack capability. Our work raises new challenges for the secure deployment of FL systems.
Tao Liu, Jiguang Lv, D. Man et al.· Cybersecurity· 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.
The proliferation of Internet of Things (IoT) devices has amplified the attack surface for large-scale cyber threats, with rapidly evolving malware families such as Mirai posing significant detection challenges. Existing side-channel and host-based approaches are limited by poor generalization to unseen variants and the absence of robust privacy protections during training. In this paper, we propose SecShield, a federated deep learning framework for privacy-preserving malware detection in resource-constrained IoT environments. SecShield employs a three-party secure computation model, where clients locally train a shared deep neural network on power side-channel traces and transmit only noisy parameter updates to a central server via an aggregator, preventing raw data exposure. Differential privacy is incorporated by injecting calibrated noise into gradients, mitigating inference risks such as data reconstruction and poisoning attacks. This decentralized architecture enables continuous adaptation to zero-day malware, reduces communication overhead, and ensures robustness against adversarial manipulation. Experimental evaluations on IoT power trace datasets demonstrate that SecShield achieves high detection accuracy over 98% while preserving client data confidentiality and maintaining computational efficiency.
Damodar Dhital, Sabir Ahmed Khan, A.L.M.U.S.T.A.P.H.A.A. Wakili et al.· International Conference on...· 0 citations
This review underscores the potential of FL to become a foundational technology in next-generation cybersecurity systems, enabling scalable and privacy-preserving threat mitigation across distributed infrastructures.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
