论文标题
通过数据选择提高联合学习的多边看法的效率
Enhancing Efficiency in Multidevice Federated Learning through Data Selection
论文作者
论文摘要
无处不在的可穿戴设备可访问多种数据。但是，对我们设备的移动性需求自然会对其计算和通信能力施加限制。一种解决方案是从无处不在的设备捕获的数据中局部学习知识，而不是以其原始形式存储和传输数据。在本文中，我们开发了一个称为Centaur的联合学习框架，以合并边缘的设备数据选择，该框架可以通过在同一用户的多元化生态系统中的约束和机智的设备之间进行协作，从而可以基于分区对深层神经网的培训。我们基于五个神经网架构和六个数据集进行基准测试，其中包括图像数据和可穿戴传感器时间序列。平均而言，与基线相比，Centaur的分类准确性高约19％，联邦训练潜伏期降低了约58％。我们还评估了CentaR在处理不平衡的非IID数据，客户参与异质性和不同的移动性模式时评估。为了鼓励在这一领域进行进一步的研究，我们在https://github.com/nokia-bell-labs/data-centric-federated-learning上发布代码
Ubiquitous wearable and mobile devices provide access to a diverse set of data. However, the mobility demand for our devices naturally imposes constraints on their computational and communication capabilities. A solution is to locally learn knowledge from data captured by ubiquitous devices, rather than to store and transmit the data in its original form. In this paper, we develop a federated learning framework, called Centaur, to incorporate on-device data selection at the edge, which allows partition-based training of a deep neural nets through collaboration between constrained and resourceful devices within the multidevice ecosystem of the same user. We benchmark on five neural net architecture and six datasets that include image data and wearable sensor time series. On average, Centaur achieves ~19% higher classification accuracy and ~58% lower federated training latency, compared to the baseline. We also evaluate Centaur when dealing with imbalanced non-iid data, client participation heterogeneity, and different mobility patterns. To encourage further research in this area, we release our code at https://github.com/nokia-bell-labs/data-centric-federated-learning
