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.

翻译：联邦学习（FL）是一种广泛采纳的从分布式移动数据中提炼人工智能的范式。然而，FL在移动网络中的部署可能因来自相邻小区或干扰源的干扰而受损。现有干扰抑制技术需要多小区协作或至少干扰信道状态信息，这在实践中代价高昂。另一方面，将干扰视为噪声的功率控制方法可能因功率预算有限而效果不佳，且该机制可能触发干扰源的对抗措施。作为一种保护FL免受干扰的实用方法，我们提出频谱呼吸，它级联随机梯度剪枝和扩频技术，在无需带宽扩展的情况下抑制干扰。其代价是通过利用剪枝导致的学习速度温和退化，提高学习延迟。我们同步这两种操作，使得它们的水平由同一参数——呼吸深度控制。为优化控制该参数，我们开发了一种基于鞅的方法，用于带频谱呼吸的空中FL的收敛分析，称为AirBreathing FL。我们展示了由呼吸深度调控的梯度剪枝与干扰引入误差之间的性能权衡。给定接收信干比和模型大小，权衡优化产生两种控制呼吸深度的方案，可固定或自适应于信道和学习过程。实验表明，在传统空中FL因强干扰而无法收敛的场景中，采用固定或自适应呼吸深度的AirBreathing FL均能确保收敛，其中自适应方案实现了接近理想性能。