Jamming Attacks on Federated Learning in Wireless Networks. (arXiv:2201.05172v1 [cs.LG])

Federated learning (FL) offers a decentralized learning environment so that a
group of clients can collaborate to train a global model at the server, while
keeping their training data confidential. This paper studies how to launch
over-the-air jamming attacks to disrupt the FL process when it is executed over
a wireless network. As a wireless example, FL is applied to learn how to
classify wireless signals collected by clients (spectrum sensors) at different
locations (such as in cooperative sensing). An …

arxiv attacks federated learning learning networks