PREFed: An Effective and Stealthy Static-Anchor Backdoor Attack via Trigger Pre-Optimization in Federated Learning

PREFed is a backdoor attack (a method to secretly inject malicious behavior into AI models) designed for federated learning (a distributed machine learning approach where multiple parties train a model together without sharing raw data). Unlike previous attacks that continuously adapt their malicious updates during training, PREFed pre-optimizes its trigger patterns (the inputs that activate the backdoor) before training starts, making the attack harder to detect while reducing computational overhead.