Federated FCM: Clustering Under Privacy Requirements
Federated FCM: Clustering Under Privacy Requirements
复制标题
联合 FCM：隐私要求下的集群
DOI：
--
复制
发表时间：
2021
影响因子：
11.9
通讯作者：
W. Pedrycz
中科院分区：
文献类型：
--
作者：
W. Pedrycz
联邦学习解决了在隐私和安全约束下实现机器学习的问题。虽然已经有深入的研究建立和分析联邦回归模型，这一主题还没有分析到目前为止，在模糊系统领域。为了缩小这一差距，在这项研究中，我们制定和解决了无监督联邦学习的问题，通过设计一个原始的联邦FCM（F-FCM）聚类，它可以作为一个基础，对建立一个频谱的模糊集结构，包括基于规则的模型。遵循一般的客户端-服务器结构，其中驻留在每个客户端的本地数据不是全局可用的，并且不能集中（如在学习场景中通常遇到的），目的是发现所有数据的整体结构。开发了在水平模式下实现的基于联邦梯度的优化。一个整体的学习过程，这是由来自客户端的通信梯度，并在服务器端提供更新的原型，并将它们传递给客户端。它还表明，方便的颗粒足迹的F-FCM构建的原型方面的全球构造的结构的相关性进行评估。一些说明性的例子来说明开发的联邦算法的效率。
Federated learning addresses the issue of machine learning realized under constraints of privacy and security. While there have been intensive studies on building and analyzing federated regression models, this topic has not been analyzed so far in the area of fuzzy systems. To narrow down this gap, in this study, we formulate and solve a problem of unsupervised federated learning by designing an original federated FCM (F-FCM) clustering which could serve as a basis toward building a spectrum of fuzzy set constructs including rule-based models. Following a general client–server structure, where the local data residing with each client are not available globally and cannot be centralized (as commonly encountered in learning scenarios), the aim is to discover an overall structure across all data. The federated gradient-based optimization realized in the horizontal mode is developed. An overall learning process is derived, which is composed of communicating gradients coming from clients and providing updates of the prototypes at the server side and passing them on to the clients. It is also shown that the relevance of the globally constructed structure is conveniently assessed in terms of granular footprints of the prototypes constructed by the F-FCM. Some illustrative examples are covered to illustrate the efficiency of the developed federated algorithm.