Computer Science · Artificial Intelligence
Researchers tested a training-free method for protecting sensitive examples used by a multimodal AI system. On smart-grid and chest-image tasks, it retained more classification accuracy than other privacy-preserving approaches across the privacy settings studied.
Researchers describe an attack on personalized federated-learning systems that aims to fool selected client models without disrupting designated “friend” models. In the main test setup, it succeeded against at least 88% of target cases while preserving at least 81% accuracy on friend models, though performance varied with the information available to the attacker.
Researchers developed an attack that generates backdoor triggers tailored to a shared AI model in federated learning. In experiments across multiple datasets, it improved the attack success rate by 60.95–80.19% over average baseline performance without increasing the malicious updates sent to the system.
