论文标题
实用的垂直联合学习，无监督的代表学习
Practical Vertical Federated Learning with Unsupervised Representation Learning
论文作者
论文摘要
随着社会对数据隐私最近的关注，我们目睹了各个应用程序中多个各方之间的数据孤岛。联合学习是一种新的学习范式，使多方能够在不共享其原始数据的情况下协作训练机器学习模型。垂直的联合学习，每个方拥有同一组样本的不同特征，并且只有一个政党具有标签，这是联邦学习的一个重要且充满挑战的话题。不同各方之间的沟通成本一直是实用垂直学习系统的主要障碍。在本文中，我们提出了一种名为Fedonce的新型沟通垂直联合学习算法，该算法仅需要各方之间的一次性交流。为了提高模型的准确性并提供隐私保证，Fedonce根据瞬间会计师在联合环境和隐私保护技术中提供了无监督的学习表征。在10个数据集上进行的全面实验表明，与最先进的垂直联合学习算法相比，Fedonce取得了近距离的性能，沟通成本要低得多。同时，我们的隐私技术在相同的隐私预算下的最先进方法大大优于最先进的方法。
As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications. Federated learning emerges as a new learning paradigm that enables multiple parties to collaboratively train a machine learning model without sharing their raw data. Vertical federated learning, where each party owns different features of the same set of samples and only a single party has the label, is an important and challenging topic in federated learning. Communication costs among different parties have been a major hurdle for practical vertical learning systems. In this paper, we propose a novel communication-efficient vertical federated learning algorithm named FedOnce, which requires only one-shot communication among parties. To improve model accuracy and provide privacy guarantee, FedOnce features unsupervised learning representations in the federated setting and privacy-preserving techniques based on moments accountant. The comprehensive experiments on 10 datasets demonstrate that FedOnce achieves close performance compared to state-of-the-art vertical federated learning algorithms with much lower communication costs. Meanwhile, our privacy-preserving technique significantly outperforms the state-of-the-art approaches under the same privacy budget.
