Spectrum Breathing: Protecting Over-the-Air Federated Learning Against
  Interference
Zhanwei Wang Kaibin Huang Yonina C. Eldar
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 can trigger countermeasures by
interference sources. As a practical approach for protecting FL against
interference, we propose Spectrum Breathing, which cascades stochastic-gradient
pruning and spread spectrum to suppress interference without bandwidth
expansion. The cost is higher learning latency by exploiting the graceful
degradation of learning speed due to pruning. We synchronize the two operations
such that their levels are controlled by the same parameter, Breathing Depth.
To optimally control the parameter, we develop a martingale-based approach to
convergence analysis of Over-the-Air FL with spectrum breathing, termed
AirBreathing FL. We show a performance tradeoff between gradient-pruning and
interference-induced error as regulated by the breathing depth. Given receive
SIR and model size, the optimization of the tradeoff yields two schemes for
controlling the breathing depth that can be either fixed or adaptive to
channels and the learning process. As shown by experiments, in scenarios where
traditional Over-the-Air FL fails to converge in the presence of strong
interference, AirBreahing FL with either fixed or adaptive breathing depth can
ensure convergence where the adaptive scheme achieves close-to-ideal
performance.