iSample: Intelligent Client Sampling in Federated Learning
iSample: Intelligent Client Sampling in Federated Learning
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DOI：
10.1109/icfec54809.2022.00015
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
H. Imani;Jeff Anderson;T. El-Ghazawi
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文献类型：
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作者：
H. Imani;Jeff Anderson;T. El-Ghazawi
人工智能在社会中的普及使机器学习(ML)成为移动和物联网(IoT)设备的宝贵工具。虽然这些设备产生的数据总量足以训练一个准确的模型，但任何一个设备可用的数据都是有限的。因此，在任何设备上使用与其余设备相关的观察经验来增强学习将是必要的。然而，这可能会显著增加带宽需求。先前的工作导致了联合学习(FL)的发展，其中客户端设备只能共享权重来相互学习，而不是交换数据。然而，设备资源可用性和网络条件的异质性仍然对训练性能造成限制。为了在保持良好精度的同时提高性能，我们引入了iSample。ISample是一种智能采样技术，通过联合考虑已知网络性能和模型质量参数来选择客户，从而使培训时间最小化。我们将iSample与其他联合学习方法进行了比较，结果表明iSample改进了全局模型的性能，特别是在训练的早期阶段，同时将CNN和VGG的训练时间分别减少了27%和39%。
The pervasiveness of AI in society has made machine learning (ML) an invaluable tool for mobile and internet-of-things (IoT) devices. While the aggregate amount of data yielded by those devices is sufficient for training an accurate model, the data available to any one device is limited. Therefore, augmenting the learning at any of the devices with the experience from observations associated with the rest of the devices will be necessary. This, however, can dramatically increase the bandwidth requirements. Prior work has led to the development of Federated Learning (FL), where instead of exchanging data, client devices can only share weights to learn from one another. However, het-erogeneity in device resource availability and network conditions still impose limitations on training performance. In order to improve performance while maintaining good levels of accuracy, we introduce iSample. iSample, an intelligent sampling technique, selects clients by jointly considering known network performance and model quality parameters, allowing the minimization of training time. We compare iSample with other federated learning approaches and show that iSample improves the performance of the global model, especially in the earlier stages of training, while decreasing the training time for both CNN and VGG by 27% and 39%, respectively.