Federated Clustering with Unknown Number of Clusters

Rong Zou, Yunfan Zhang, Yiqun Zhang, Yang Lu, Mengke Li, Yiu Ming Cheung*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)

Abstract

Federated clustering is crucial to mining knowledge from unlabeled data distributed to multiple clients while preserving privacy. As there is no explicit learning supervision, clustering is considered a challenging federated learning task. Most existing works assume that the 'true' cluster number k∗ is given to each client and server, which is far from a real federated learning scenario. Without the guidance of k∗, federated clustering becomes more challenging, rendering most existing solutions infeasible. We therefore propose a Federated Competitive and Cooperative Learning mechanism (FedCCL) to explore and fuse heterogeneous cluster distributions from clients automatically, and eventually form a global cluster partition, without requiring the cluster number to be given. We let the clients download seed points to explore their local distributions, which are then uploaded to the server for fusion. Different clients are allowed to compete on a single seed to form a consensus, while close seeds cooperate to represent a cluster. By iteratively homogenizing the cooperated seeds, a proper number of clusters will gradually emerge. Extensive experiments demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2024 6th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2024
PublisherIEEE
Pages671-677
Number of pages7
ISBN (Electronic)9798350377842, 9798350377835
ISBN (Print)9798350377859
DOIs
Publication statusPublished - 16 Aug 2024
Event6th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2024 - Hangzhou, China
Duration: 16 Aug 202418 Aug 2024

Publication series

NameInternational Conference on Data-Driven Optimization of Complex Systems
PublisherIEEE

Conference

Conference6th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2024
Country/TerritoryChina
CityHangzhou
Period16/08/2418/08/24

Scopus Subject Areas

  • Computer Science Applications
  • Information Systems and Management
  • Automotive Engineering
  • Control and Optimization

User-Defined Keywords

  • Competitive and Coopera-tive Learning
  • Federated Clustering
  • Unknown Number of Clusters

Fingerprint

Dive into the research topics of 'Federated Clustering with Unknown Number of Clusters'. Together they form a unique fingerprint.

Cite this