Towards Communication-Learning Trade-off for Federated Learning at the
Network Edge
- URL: http://arxiv.org/abs/2205.14271v1
- Date: Fri, 27 May 2022 23:11:52 GMT
- Title: Towards Communication-Learning Trade-off for Federated Learning at the
Network Edge
- Authors: Jianyang Ren, Wanli Ni, and Hui Tian
- Abstract summary: We propose a wireless learning (FL) system where network pruning is applied to local users with limited resources.
Although beneficial to FL latency, it also deteriorates information loss.
- Score: 5.267288702335319
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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