Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference. (arXiv:2305.05933v1 [cs.LG])

Federated Learning (FL) is a widely embraced paradigm for distilling
artificial intelligence from distributed mobile data. However, the deployment
of FL in mobile networks can be compromised by exposure to interference from
neighboring cells or jammers. Existing interference mitigation techniques
require multi-cell cooperation or at least interference channel state
information, which is expensive in practice. On the other hand, power control
that treats interference as noise may not be effective due to limited power
budgets, and also that this mechanism …

artificial artificial intelligence channel compromised cooperation data deployment distributed exposure federated learning information intelligence interference mitigation mobile mobile networks networks paradigm protecting spectrum state techniques