AntiFLipper: A Secure and Efficient Defense against Label-Flipping Attacks in Federated Learning

Federated learning FL enables privacy-preserving model training by keeping data decentralized. However, it remains vulnerable to label-flipping attacks, where malicious clients manipulate labels to poison the global model. Despite their simplicity, these attacks can severely degrade model...

A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning

Federated learning FL enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-poisoning attacks, in which malicious actors submit corrupted model updates to degrade the global model. Existing defens...

Safeguarding Federated Learning-Based Road Condition Classification

Federated Learning FL has emerged as a promising solution for privacy-preserving autonomous driving, specifically camera-based Road Condition Classification RCC systems, harnessing distributed sensing, computing, and communication resources on board vehicles without sharing sensitive image data...