Supporting data for “Optimizing Federated Learning with Communication Reduction and Synchronization Control”

Experimental data used in Thesis  “Optimizing Federated Learning with Communication Reduction and Synchronization Control”.
There are source codes that read the logs from the 'data/' 
folder and then display the figures or tables.

This thesis aims to improve the federated learning systems with communication reduction techniques and accelerate model convergence speed by asynchronous training. 
The provided data show the effectiveness of our proposed algorithm, which includes two fast K asynchronous algorithms and a novel weighted aggregation function.

本数据集收录了论文《通过通信减少与同步控制优化联邦学习》中使用的实验数据。该论文旨在通过通信减少技术提升联邦学习系统性能，并通过异步训练加速模型收敛速度。数据集展示了所提出算法的有效性，该算法包括两种快速异步K算法以及一种创新的加权聚合函数。