In this paper, we focus on effective learning over a collaborative research network involving multiple clients. Each client has its own sample population which may not be shared with other clients due to privacy concerns. The goal is to learn a model for each client, which behaves better than the one learned from its own data, through secure collaborations with other clients in the network. Due to the discrepancies of the sample distributions across different clients, it is not necessarily that collaborating with everyone will lead to the best local models. We propose a learning to collaborate framework, where each client can choose to collaborate with certain members in the network to achieve a "collaboration equilibrium", where smaller collaboration coalitions are formed within the network so that each client can obtain the model with the best utility. We propose the concept of benefit graph which describes how each client can benefit from collaborating with other clients and develop a Pareto optimization approach to obtain it. Finally the collaboration coalitions can be derived from it based on graph operations. Our framework provides a new way of setting up collaborations in a research network. Experiments on both synthetic and real world data sets are provided to demonstrate the effectiveness of our method.
翻译:在本文中,我们侧重于通过涉及多个客户的协作研究网络进行有效学习。每个客户都有自己的抽样人口,由于隐私问题,可能无法与其他客户共享。目标是通过与网络中其他客户的可靠合作,为每个客户学习一个模式,其表现优于从自身数据中学习的模式。由于抽样分布在不同客户之间的差异,与每个人的合作不一定导致最佳的地方模式。我们建议学习协作框架,每个客户可以选择与网络中某些成员合作,以实现“合作平衡”,在网络中组成较小的协作联盟,以便每个客户都能以最佳的效用获得模型。我们提出了利益图概念,说明每个客户如何从与其他客户的合作中获益,并开发一个Pareto优化方法来获得它。最后,合作联盟可以借助图表操作来建立。我们的框架提供了在研究网络中建立协作的新方式。对合成和真实世界数据集进行实验是为了证明我们的方法的有效性。