  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.
