Jul 2026· Annual International Computer Software and Applications Conference· pp. 2387-2391· 0 citations· 16 references
Abstract
Federated Learning (FL) is a new way to train machine learning models across many decentralized devices while keeping data private. In Intrusion Detection Systems (IDS), FL allows different networks to share security intelligence without sharing sensitive raw data. However, this decentralized structure makes the system vulnerable to security risks, especially poisoning attacks. In these attacks, malicious clients provide fake updates to damage the global model's accuracy. This paper studies how to defend FL-based IDS against these threats. We evaluate two main defenses: robust aggregation techniques and client selection strategies. Our experiments show that these methods successfully maintain high IDS performance even when many clients are compromised. The findings offer a practical guide for building secure and privacy-preserving intrusion detection systems
Federated learning (FL) is a machine learning technique where multiple clients with local data collaborate in training a machine learning model. However, this centralization of sensitive model updates incurs security and privacy issues. A malicious aggregator can launch inference attacks to violate the privacy of clien...
Jia-Ming Fang· 2026 3rd World Conference on...· 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.
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...
This study proposes a lightweight defense mechanism for mitigating poisoning attacks in Federated Learning (FL) systems that involves transforming model weights into the frequency domain to identify core frequency components containing sufficient model weight information.
A. R., D. Renuka· Bulletin of the Polish Acade...· 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.
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
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