Federated learning, which solves the problem of data island by connecting multiple computational devices into a decentralized system, has become a promising paradigm for privacy-preserving machine learning. This paper studies vertical federated learning (VFL), which tackles the scenarios where collaborating organizations share the same set of users but disjoint features. Contemporary VFL methods are mainly used in static scenarios where the active party and the passive party have all the data from the beginning and will not change. However, the data in real life often changes dynamically. To alleviate this problem, we propose a new vertical federation learning method, DVFL, which adapts to dynamic data distribution changes through knowledge distillation. In DVFL, most of the computations are held locally to improve data security and model efficiency. Our extensive experimental results show that DVFL can not only obtain results close to existing VFL methods in static scenes, but also adapt to changes in data distribution in dynamic scenarios.
翻译:联邦学习通过将多种计算装置连接到分散的系统来解决数据岛问题,已成为保护隐私机器学习的一个有希望的范例。本文研究纵向联合学习(VFL),它涉及合作组织共用同一组用户但互不相连特点的情景。当代VFL方法主要用于静止的情景中,即活跃方和被动方从一开始就拥有所有数据,不会改变。然而,现实生活中的数据往往动态地变化。为了缓解这一问题,我们提议一种新的纵向联邦学习方法DVFL,即DVFL,通过知识蒸馏适应动态数据分配变化。在DVFL,大多数计算都在当地进行,以提高数据安全和模型效率。我们广泛的实验结果表明,DVFL不仅能够在静态场上获得接近现有VFL方法的结果,而且还适应动态情景中数据分配的变化。