  Federated learning (FL) is particularly useful in wireless networks due to
its distributed implementation and privacy-preserving features. However, as a
distributed learning system, FL can be vulnerable to malicious attacks from
both internal and external sources. Our work aims to investigate the attack
models in a FL-enabled wireless networks. Specifically, we consider a cell
sleep control scenario, and apply federated reinforcement learning to improve
energy-efficiency. We design three attacks, namely free rider attacks,
Byzantine data poisoning attacks and backdoor attacks. The simulation results
show that the designed attacks can degrade the network performance and lead to
lower energy-efficiency. Moreover, we also explore possible ways to mitigate
the above attacks. We design a defense model called refined-Krum to defend
against attacks by enabling a secure aggregation on the global server. The
proposed refined- Krum scheme outperforms the existing Krum scheme and can
effectively prevent wireless networks from malicious attacks, improving the
system energy-efficiency performance.
