Federated Generative Model on Multi-Source Heterogeneous Data in IoT
Federated Generative Model on Multi-Source Heterogeneous Data in IoT
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DOI：
10.1609/aaai.v37i9.26252
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Zuobin Xiong;Wei Li;Zhipeng Cai
中科院分区：
文献类型：
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作者：
Zuobin Xiong;Wei Li;Zhipeng Cai
生成模型的研究是深度学习技术的一个有前途的分支，该分支已成功应用于不同场景，例如人工智能和物联网。虽然在大多数现有作品中，生成模型都是一种集中式结构，从而提高了安全性和隐私的威胁以及通信成本的覆盖率。罕见的努力一直致力于调查分布式生成模型，尤其是当培训数据来自现实的物联网设置下的多个异质来源时。在本文中，为了处理这个具有挑战性的问题，我们设计了一个联合生成模型框架，该框架可以学习层次物联网系统的强大生成器。特别是，我们的生成模型框架可以在两个方案（即功能相关的方案和与标签相关的方案）中解决多源异质数据上的分布式数据生成问题。此外，在我们的联合生成模型中，我们开发了一种同步和异步更新方法，以满足不同的应用程序要求。在模拟数据集和多个真实数据集上进行了广泛的实验，以通过与最先进的图案进行比较来评估我们提出的生成模型的数据生成模型的性能。
The study of generative models is a promising branch of deep learning techniques, which has been successfully applied to different scenarios, such as Artificial Intelligence and the Internet of Things. While in most of the existing works, the generative models are realized as a centralized structure, raising the threats of security and privacy and the overburden of communication costs. Rare efforts have been committed to investigating distributed generative models, especially when the training data comes from multiple heterogeneous sources under realistic IoT settings. In this paper, to handle this challenging problem, we design a federated generative model framework that can learn a powerful generator for the hierarchical IoT systems. Particularly, our generative model framework can solve the problem of distributed data generation on multi-source heterogeneous data in two scenarios, i.e., feature related scenario and label related scenario. In addition, in our federated generative models, we develop a synchronous and an asynchronous updating methods to satisfy different application requirements. Extensive experiments on a simulated dataset and multiple real datasets are conducted to evaluate the data generation performance of our proposed generative models through comparison with the state-of-the-arts.