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Jan, 2021
异构联邦学习中的设备采样:理论、算法和实现
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
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Su Wang, Mengyuan Lee, Seyyedali Hosseinalipour, Roberto Morabito, Mung Chiang...
TL;DR
本文提出了一种基于设备采样和设备到设备卸载组合优化的联邦学习优化算法,通过优化采样节点和数据卸载配置来最大化FedL训练的准确性,有效提高了训练模型的准确性和资源利用率。
Abstract
The conventional
federated learning
(FedL) architecture distributes
machine learning
(ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two imp
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