Robust Federated Learning for Wireless Networks: A Demonstration with
  Channel Estimation
Zexin Fang Bin Han Hans D. Schotten
Federated learning (FL) offers a privacy-preserving collaborative approach
for training models in wireless networks, with channel estimation emerging as a
promising application. Despite extensive studies on FL-empowered channel
estimation, the security concerns associated with FL require meticulous
attention. In a scenario where small base stations (SBSs) serve as local models
trained on cached data, and a macro base station (MBS) functions as the global
model setting, an attacker can exploit the vulnerability of FL, launching
attacks with various adversarial attacks or deployment tactics. In this paper,
we analyze such vulnerabilities, corresponding solutions were brought forth,
and validated through simulation.