海报:保持ABS边缘辅助在线联邦学习的训练效率和准确性

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI:10.1109/ICNP49622.2020.9259386

Jiayu Wang, Zehua Guo, Sen Liu, Yuanqing Xia

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引用次数: 2

摘要

本文提出了一种用于在线联邦学习的自适应批大小(ABS)算法。ABS是一种迭代过程高效的解决方案，可以自适应地调整边缘节点上训练过程的批大小。初步结果表明，与现有的迭代循环效率解决方案相比，ABS保持了训练效率和精度。

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Poster: Maintaining Training Efficiency and Accuracy for Edge-assisted Online Federated Learning with ABS

This paper proposes Adaptive Batch Sizing (ABS) for online federated learning. ABS is an iteration process-efficient solution that adaptively adjusts batch size of the training process at edge nodes. Preliminary results show that ABS maintains training efficiency and accuracy, compared with existing iteration round-efficient solutions.

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2020 IEEE 28th International Conference on Network Protocols (ICNP)

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