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Nov, 2023
资源受限异构设备上的联邦学习的混合精度量化
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
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Huancheng Chen, Haris Vikalo
TL;DR
引入混合精度量化方法到异构资源联邦学习系统中以解决通信和计算瓶颈问题,并在多个模型架构和数据集上进行了广泛的基准性实验验证其优于固定精度量化的性能。
Abstract
While
federated learning
(FL) systems often utilize
quantization
to battle communication and computational bottlenecks, they have heretofore been limited to deploying fixed-precision
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