Federated Learning in Cybersecurity: Enhancing Decentralized Threat Detection

SecureFedShield: An Adaptive Privacy-Preserving Federated Defense Framework Against Adversarial Attacks in Financial Fraud Detection

SecureFedShield is proposed, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments that integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture.

Efficient Backdoor Mitigation in Federated Learning With Contrastive Loss

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.

Active Digital Twin Verification for Robust Federated Learning in IoT Intrusion Detection

Federated Learning has become a practical approach for training intrusion detection models across distributed Internet of Things devices, but it remains exposed to poisoning attacks, non-IID data heterogeneity, and free-rider exploitation. This paper presents DT-Guard, a defense framework that leverages a server-side Digital Twin as a controlled testing environment for actively verifying client model behavior. Each submitted update is deployed in the Digital Twin and evaluated on synthetic challenge data through a four-layer pipeline that examines detection capability, backdoor resistance, parameter deviation, and cross-round stability. A complementary aggregation scheme called DT-Driven Performance Weighting compares client predictions against the current global model, exposing free-riders whose outputs are nearly indistinguishable from the global baseline. We validate DT-Guard on CIC-IoT-2023 under five poisoning strategies. DT-Guard generally outperforms nine existing defenses in accuracy, false positive rate, and contribution fairness.

Adaptive Intrusion Detection in WSNs Using Federated Deep Learning and Homomorphic Encryption

Wireless Sensor Networks (WSNs) play a crucial role in various applications, but their vulnerability to malicious nodes and data breaches hinders their full potential. Traditional security methods often struggle to keep pace with evolving attack patterns and can introduce privacy concerns. This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy. Sensor nodes collaboratively train adaptive deep learning models to identify anomalies in real-time, enabling continuous learning and response to evolving threats. Partial Homomorphic Encryption (PHE) safeguards sensitive data throughout the network, ensuring data confidentiality. The trade-off between security and computational cost associated with PHE is acknowledged. The effectiveness of the proposed system FedShield-PHE will be evaluated through simulations, comparing its performance to existing methods across various metrics including detection accuracy, network overhead, and energy consumption. This research offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.

FedGuard: A Federated Learning Framework for Collaborative and Privacy-Preserving Mobile Threat Intelligence

A Federated Deep Learning Paradigm for Privacy-Preserving Cloud Applications

Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequently lack accuracy, scalability, or practicality in their operations. To overcome these limitations, a federated deep learning model is formulated so that secure joint learning can occur without transferring raw data across the domains of ownership. The framework incorporates training that is decentralized, training that uses differential privacy, training that uses secure aggregation, training that uses encrypted communication, and training that uses trust-based anomaly defense to defend against leakage, poisoning, and inference attacks. It also supports heterogeneous and highly non-IID datasets using adaptive coordination and stability-relevant participation regulation and meets emerging data protection requirements. The methods of resource-conscious orchestration and the optimization of communication eliminate overhead without obstructing the effectiveness of learning. The paradigm has therefore formed a privacy-by-design intelligent cloud ecosystem which ensures confidentiality, maintains performance, enhances robustness, and ensures responsible AI implementation in privacy-related sectors of healthcare, finance, governance, and smart infrastructure.