Asynchronous Federated Clustering with Unknown Number of Clusters
Yunfan Zhang, Yiqun Zhang, Yang Lu, Mengke Li, Xi Chen, Yiu-ming, Cheung

TL;DR
This paper introduces AFCL, a novel asynchronous federated clustering method that handles unknown cluster numbers and client heterogeneity, improving clustering accuracy while preserving privacy in non-IID data scenarios.
Contribution
It proposes an innovative AFCL approach that manages asynchronous client updates and unknown cluster counts, filling gaps in federated clustering research.
Findings
AFCL effectively handles asynchronous client communication.
The method accurately estimates the number of clusters.
Experiments show superior performance over existing methods.
Abstract
Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data provided by multiple clients while preserving their privacy. Most existing attempts learn cluster distributions at local clients, and then securely pass the desensitized information to the server for aggregation. However, some tricky but common FC problems are still relatively unexplored, including the heterogeneity in terms of clients' communication capacity and the unknown number of proper clusters . To further bridge the gap between FC and real application scenarios, this paper first shows that the clients' communication asynchrony and unknown are complex coupling problems, and then proposes an Asynchronous Federated Cluster Learning (AFCL) method accordingly. It spreads the excessive number of seed points to the clients as a learning medium and…
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Taxonomy
TopicsComplex Network Analysis Techniques · Caching and Content Delivery · Cooperative Communication and Network Coding
