Poison-Resilient and Privacy-Preserving Federated Learning Scheme in Mobile Systems

On the Limits of Privacy in Federated Learning: Personalization and Adversarial Analysis

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.

Trustworthy and privacy-preserving decentralized federated learning with multi-layer defense for secure collaborative AI

These findings demonstrate that the proposed framework provides an effective balance between privacy preservation, adversarial robustness, and trustworthy decentralized collaborative learning for secure AI-driven systems.

FedShield: A Privacy-Preserving Federated Learning Framework for Behavioral Detection and Mitigation of Zero-Day Attacks

Zero-day is a type of attack that targets vulnerabilities unknown to vendors and security experts. Traditional signature-based intrusion detection systems fail to flag such attacks. ML models used to detect attacks require data aggregation, which raises substantial privacy concerns and infringes on an organization’s da...

Data Poisoning Detection for Secure Federated Learning With Dropout-Resilient Aggregation

Federated learning (FL) enables multiple clients to train a global model collaboratively without sharing their raw data. However, compromised clients can launch data poisoning attacks by submitting malicious local updates and thereby degrade model accuracy. In addition, unprotected model updates may disclose sensitive...

Big data privacy protection and data security based on deep learning

A federated deep learning framework that systematically integrates adaptive privacy noise mechanisms and trust-weighted aggregation within a distributed architecture that ensures the protection of sensitive data during collaborative analysis through precise differential privacy control and advanced neural network model...