论文标题
迈向联合聚类：联合模糊$ C $ -MEANS算法（FFCM）
Towards Federated Clustering: A Federated Fuzzy $c$-Means Algorithm (FFCM)
论文作者
论文摘要
联合学习（FL）是一个环境，在培训联合机器学习（ML）模型中进行了分布式数据协作的多个方面，同时将所有数据保留在各方。联合聚类是FL内的一个研究领域，它与将所有数据保持本地数据的同时将全球相似的数据分组在一起。我们描述了这一研究领域如何本身感兴趣，或者如何帮助解决受监督的FL框架中非独立的相同分布（I.I.D.）等问题。但是，这项工作的重点是将联合模糊$ C $ -MEANS算法扩展到FL设置（FFCM），作为对联合聚类的贡献。我们提出了两种计算全球群集中心并通过具有挑战性的数值实验评估其行为的方法。我们观察到，即使在具有挑战性的情况下，其中一种方法也能够识别出良好的全球集群，但也承认许多挑战仍然开放。
Federated Learning (FL) is a setting where multiple parties with distributed data collaborate in training a joint Machine Learning (ML) model while keeping all data local at the parties. Federated clustering is an area of research within FL that is concerned with grouping together data that is globally similar while keeping all data local. We describe how this area of research can be of interest in itself, or how it helps addressing issues like non-independently-identically-distributed (i.i.d.) data in supervised FL frameworks. The focus of this work, however, is an extension of the federated fuzzy $c$-means algorithm to the FL setting (FFCM) as a contribution towards federated clustering. We propose two methods to calculate global cluster centers and evaluate their behaviour through challenging numerical experiments. We observe that one of the methods is able to identify good global clusters even in challenging scenarios, but also acknowledge that many challenges remain open.
