Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
This addresses a critical security problem for wireless network operators and applications like IoT and autonomous vehicles, but it is incremental as it builds on existing FL security research.
The paper tackles the security vulnerability of Federated Learning-based wireless traffic prediction systems by introducing a fake traffic injection attack that undermines the system with minimal knowledge, and proposes a global-local inconsistency detection defense that removes abnormal model parameters, with both strategies significantly outperforming existing baselines in experiments on real-world datasets.
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 of FL-based distributed wireless systems, particularly in regression-based WTP problems, remain inadequately investigated. In this paper, we introduce a novel fake traffic injection (FTI) attack, designed to undermine the FL-based WTP system by injecting fabricated traffic distributions with minimal knowledge. We further propose a defense mechanism, termed global-local inconsistency detection (GLID), which strategically removes abnormal model parameters that deviate beyond a specific percentile range estimated through statistical methods in each dimension. Extensive experimental evaluations, performed on real-world wireless traffic datasets, demonstrate that both our attack and defense strategies significantly outperform existing baselines.