Towards Communication-Learning Trade-off for Federated Learning at the Network Edge
This work addresses efficiency challenges for resource-limited edge devices in federated learning, though it is incremental as it builds on existing pruning and optimization methods.
The paper tackles the trade-off between communication latency and learning performance in wireless federated learning by applying network pruning, deriving a convergence rate to quantify effects, and proposing closed-form solutions for pruning control and bandwidth allocation. The results show the solution outperforms benchmarks in cost reduction and accuracy, with pruning reducing overhead but worsening accuracy.
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.