Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning. In this work, we mainly focus on the optimization of computation and communication in FL from a view of pruning. By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models. Two specific schemes of FedLP are designed for scenarios with homogeneous local models and heterogeneous ones. Both theoretical and experimental evaluations are developed to verify that FedLP relieves the system bottlenecks of communication and computation with marginal performance decay. To the best of our knowledge, FedLP is the first framework that formally introduces the layer-wise pruning into FL. Within the scope of federated learning, more variants and combinations can be further designed based on FedLP.

本文介绍一种针对分布式学习中计算和通信优化的显式FL剪枝框架(FedLP)，采用层次剪枝在本地训练和协作更新中，两种特定方案的实验验证表明FedLP可以减轻通信和计算瓶颈并具有良好的性能。FedLP是将层次剪枝正式引入FL的第一个框架。

FedLP：面向通信计算效率的层次剪枝联邦学习机制