论文标题
在复制内核希尔伯特空间中使用副函数的联合分类
Federated Classification using Parsimonious Functions in Reproducing Kernel Hilbert Spaces
论文作者
论文摘要
联合学习构成了使用从联邦代理人收集的数据的全球模型。这种学习类型有两个主要挑战：代理通常不会在同一分布上收集数据，并且代理的存储和传输数据功能有限。因此，每个代理商通过网络发送整个数据是不切实际的。取而代之的是，每个代理必须形成本地模型，并确定哪些信息对学习问题至关重要，该问题将发送到中央单位。然后，中央单元只能使用代理商收到的信息形成全局模型。我们提出了一种解决这些挑战的方法。首先，每个代理使用低复杂性重现Hilbert空间表示形式的局部模型。从模型中，代理商确定了发送到中央单元的基本样本。通过解决偶性问题获得基本样本。然后，中央单元形成全球模型。我们表明，随着样本量的增加，联合学习者的解决方案会渐近地收敛到集中学习者的解决方案。使用具有模拟数据和实际数据集的实验评估了所提出的算法的性能，并从活动识别任务中进行了实际数据集，从而从可穿戴设备中收集数据。实验结果表明，我们方法的准确性会收敛到增加样本量增加的集中学习者的准确性。
Federated learning forms a global model using data collected from a federation agent. This type of learning has two main challenges: the agents generally don't collect data over the same distribution, and the agents have limited capabilities of storing and transmitting data. Therefore, it is impractical for each agent to send the entire data over the network. Instead, each agent must form a local model and decide what information is fundamental to the learning problem, which will be sent to a central unit. The central unit can then form the global model using only the information received from the agents. We propose a method that tackles these challenges. First each agent forms a local model using a low complexity reproducing kernel Hilbert space representation. From the model the agents identify the fundamental samples which are sent to the central unit. The fundamental samples are obtained by solving the dual problem. The central unit then forms the global model. We show that the solution of the federated learner converges to that of the centralized learner asymptotically as the sample size increases. The performance of the proposed algorithm is evaluated using experiments with both simulated data and real data sets from an activity recognition task, for which the data is collected from a wearable device. The experimentation results show that the accuracy of our method converges to that of a centralized learner with increasing sample size.
