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.
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.
Kriti Mishra· International Journal of Cre...· 0 citations
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...
Hal Ferguson, Rui Ning, Hongyi Wu et al.· IEEE Internet of Things Jour...· 0 citations
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 D...
H. Pham, Duy The Phan, Van-Hau Pham· IEEE International Conferenc...· 0 citations
This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy, and offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.
N. Karthick, K. R. Singh· International journal of com...· 0 citations
This research hopes to alleviate some of these challenges by enabling collaborative model training across remote devices while assuring that confidential user data remains uncompromised and undisclosed and inspires service providers, vendors of devices and researchers to the utility of elevating mobile security.
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 frequent...
Sribidhya Mohanty, Pallavi Gupta, Anil Pratap Singh et al.· 2026 International Conferenc...· 0 citations
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