Detection and Mitigation Data Poisoning Attacks in Multimodal Online Federated Learning

Multimodal Online Federated Learning (MMO-FL, a system where multiple IoT devices with different types of sensors train AI models together in real-time without sending raw data to a central server) faces security risks from data poisoning attacks (when attackers inject corrupted or malicious data into the training process). Researchers studied these attacks across three vulnerability dimensions (distributed systems, real-time learning, and multiple data types) and proposed a detection and mitigation algorithm tested on real-world datasets to defend against them.