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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Federated Classification with Low Complexity Reproducing Kernel Hilbert Space Representations
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Federated Classification with Low Complexity Reproducing Kernel Hilbert Space Representations

机译：低复杂度的联合分类重现内核希尔伯特空间表示

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In federated learning, a centralized model is realized based on information received from a group of agents each collecting data. This setting has two major challenges: the agents observe data over different distributions and they have only limited capabilities of sending data over the network to the centralized unit. Therefore, sending all the training data over the network is impractical. Each agent must train its own model and decide what relevant information it needs to send to the centralized unit. In this work we propose a method for federated learning in which each agent learns a low complexity Reproducing kernel Hilbert space representation. Leveraging the zero duality gap and the fact that each dual variable is associated with a sample, the agent discards samples for which the optimal dual variable is zero and sends only fundamental samples to the centralized unit. The centralized unit then computes the global model. We show that as the sample size grows, the solution obtained by the central unit converges to that obtained by an omniscient classifier which has access to all samples from all agents. We illustrate the performance of our federated learning algorithm and compare it to the omniscient classifier with a simulation.
机译：在联合学习中，基于从分别收集数据的一组代理接收的信息来实现集中式模型。此设置面临两个主要挑战：代理观察不同分布上的数据，并且它们通过网络将数据发送到集中式单元的能力有限。因此，通过网络发送所有训练数据是不切实际的。每个代理必须训练自己的模型，并决定需要将哪些相关信息发送到集中式单元。在这项工作中，我们提出了一种用于联合学习的方法，其中每个代理都学习一种低复杂度的再现内核希尔伯特空间表示。利用零对偶间隙和每个对偶变量与一个样本相关联的事实，代理会丢弃其最佳对偶变量为零的样本，仅将基本样本发送到集中式单元。然后，集中单元计算全局模型。我们表明，随着样本数量的增加，由中央单元获得的解决方案收敛于由全能分类器获得的解决方案，全能分类器可以访问来自所有代理的所有样本。我们说明了联邦学习算法的性能，并通过仿真将其与全能分类器进行了比较。

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