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Adaptive Network Pruning for Wireless Federated Learning


Abstract:

In this letter, we apply the model compression, i.e., network pruning, into wireless federated learning (FL) system to mitigate the local computation and communication bo...Show More

Abstract:

In this letter, we apply the model compression, i.e., network pruning, into wireless federated learning (FL) system to mitigate the local computation and communication bottlenecks. Firstly, the convergence rate and learning latency of the FL system are mathematically analyzed. Then, an optimization problem is formulated to maximize the convergence rate while guaranteeing the learning latency via jointly optimizing the pruning ratio and spectrum allocation. Finally, the experimental results show that the proposed learning scheme can improve the performance of the wireless FL as compared with other conventional schemes.
Published in: IEEE Wireless Communications Letters ( Volume: 10, Issue: 7, July 2021)
Page(s): 1572 - 1576
Date of Publication: 20 April 2021

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