personalized-federated-learning-with-model-quantization's Introduction

PFL-MQ

个性化联邦学习+模型量化

解决问题

在PS架构下，模型训练时间长、通信开销大以及同步屏障的问题，提高在non-IID数据分布上的训练精度。（固定时间，不同non-IID程度下能够达到多少的精度；固定通信开销，不同non-IID程度下能够达到多少的精度；每一轮的等待时间；指定精度，需要的通信开销。）

方法

通过动态确定每一轮的量化比的方法降低等待时间，缓解同步屏障。同时根据数据分布和量化比确定模型聚合的权重。

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