Skip to content
Log in
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

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

Satyashree
· 0 citations · 16 references

TL;DR

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.

Semantic Scholar
View source

Similar papers

Conference Open access 2026

An Intelligent Intrusion Detection and Privacy-Preserving Architecture for the Internet of Medical Things (IoMT)

This work proposes an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead, and proposes an adaptive FedProx-based weighted federated learning framework.

Emmanuel Udok, B. Stephen, U. Luke et al. · 0 citations
Jul 2026

Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture

A Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture is presented, with FL for anomaly detection integrating privacy-preserving distributed learning, continuous identity verification, and LLM-driven autonomous threat response into a unified pipeline.

Amal Alshehri, Cihan Tunc · 0 citations
Review

Federated Learning in Cybersecurity: Enhancing Decentralized Threat Detection

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.

Sai Bhuvana Kurada · 0 citations
Aug 2026

A Secure Federated Learning and Blockchain Framework for E-Health Threat Detection

The proposed framework effectively integrates encryption, federated intrusion detection, explainable artificial intelligence, and blockchain security to enhance privacy, transparency, and reliability in IoMT healthcare networks.

P. Banupriya, K. Vanitha · 0 citations
Aug 2026

Federated Anomaly Detection for IoMT Networks: Privacy‐Preserving Design, Lightweight Implementation, and Runtime Evaluation

The rapid integration of Internet of Things (IoT) in the healthcare domain has led to the emergence of the Internet of Medical Things (IoMT), which introduces significant benefits in patient monitoring and real‐time medical services. However, IoMT networks are inherently vulnerable due to resource constraints, heteroge...

Mahdi Ajdani, M. Asmani, A. A. Laghari · 0 citations
Open access Aug 2026

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.

Kriti Mishra · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.