Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction

arXiv:2404.14389v1 Announce Type: cross
Abstract: Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. This makes it ideal for applications like wireless traffic prediction (WTP), which plays a crucial role in optimizing network resources, enabling proactive traffic flow management, and enhancing the reliability of downstream communication-aided applications, such as IoT devices, autonomous vehicles, and industrial automation systems. Despite its promise, the security aspects …

abstract applications arxiv attacks control cs.cr cs.lg cs.ni data distributed enabling federated learning framework global multiple network poisoning attacks prediction privacy resources role traffic train type wireless