论文标题
在网络边缘进行沟通学习的权衡取舍
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge
论文作者
论文摘要
在这封信中，我们研究了一个无线联合学习（FL）系统，该系统将网络修剪应用于资源有限的本地用户。尽管修剪有益于减少FL延迟，但由于信息损失而导致学习绩效。因此，沟通和学习之间的权衡问题得到了提出。为了应对这一挑战，我们通过推导FL的收敛速率与非convex损耗函数来量化网络修剪和数据包误差对学习性能的影响。然后，提出了用于修剪控制和带宽分配的封闭式解决方案，以最大程度地减少FL延迟和FL性能的加权总和。最后，数值结果表明，1）我们提出的解决方案可以在降低成本和准确性保证方面均优于基准，以及2）更高的修剪率将使沟通较小的开销较少，但也使FL的准确性更加恶化，这与我们的理论分析一致。
In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learning performance due to the information loss. Thus, a trade-off problem between communication and learning is raised. To address this challenge, we quantify the effects of network pruning and packet error on the learning performance by deriving the convergence rate of FL with a non-convex loss function. Then, closed-form solutions for pruning control and bandwidth allocation are proposed to minimize the weighted sum of FL latency and FL performance. Finally, numerical results demonstrate that 1) our proposed solution can outperform benchmarks in terms of cost reduction and accuracy guarantee, and 2) a higher pruning rate would bring less communication overhead but also worsen FL accuracy, which is consistent with our theoretical analysis.
