  Federated learning (FL) allows distributed participants to train machine
learning models in a decentralized manner. It can be used for radio signal
classification with multiple receivers due to its benefits in terms of privacy
and scalability. However, the existing FL algorithms usually suffer from slow
and unstable convergence and are vulnerable to poisoning attacks from malicious
participants. In this work, we aim to design a versatile FL framework that
simultaneously promotes the performance of the model both in a secure system
and under attack. To this end, we leverage attention mechanisms as a defense
against attacks in FL and propose a robust FL algorithm by integrating the
attention mechanisms into the global model aggregation step. To be more
specific, two attention models are combined to calculate the amount of
attention cast on each participant. It will then be used to determine the
weights of local models during the global aggregation. The proposed algorithm
is verified on a real-world dataset and it outperforms existing algorithms,
both in secure systems and in systems under data poisoning attacks.
