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One-Shot Hierarchical Federated Clustering

Shenghong Cai, Zihua Yang, Yang Lu, Mengke Li, Yuzhu Ji, Yiqun Zhang, Yiu-Ming Cheung

TL;DR

It turns out that the complex cluster distributions across clients can be efficiently explored, and extensive experiments comparing state-of-the-art methods on ten public datasets demonstrate the superiority of the proposed method.

Abstract

Driven by the growth of Web-scale decentralized services, Federated Clustering (FC) aims to extract knowledge from heterogeneous clients in an unsupervised manner while preserving the clients' privacy, which has emerged as a significant challenge due to the lack of label guidance and the Non-Independent and Identically Distributed (non-IID) nature of clients. In real scenarios such as personalized recommendation and cross-device user profiling, the global cluster may be fragmented and distributed among different clients, and the clusters may exist at different granularities or even nested. Although Hierarchical Clustering (HC) is considered promising for exploring such distributions, the sophisticated recursive clustering process makes it more computationally expensive and vulnerable to privacy exposure, thus relatively unexplored under the federated learning scenario. This paper introduces an efficient one-shot hierarchical FC framework that performs client-end distribution exploration and server-end distribution aggregation through one-way prototype-level communication from clients to the server. A fine partition mechanism is developed to generate successive clusterlets to describe the complex landscape of the clients' clusters. Then, a multi-granular learning mechanism on the server is proposed to fuse the clusterlets, even when they have inconsistent granularities generated from different clients. It turns out that the complex cluster distributions across clients can be efficiently explored, and extensive experiments comparing state-of-the-art methods on ten public datasets demonstrate the superiority of the proposed method.

One-Shot Hierarchical Federated Clustering

TL;DR

It turns out that the complex cluster distributions across clients can be efficiently explored, and extensive experiments comparing state-of-the-art methods on ten public datasets demonstrate the superiority of the proposed method.

Abstract

Driven by the growth of Web-scale decentralized services, Federated Clustering (FC) aims to extract knowledge from heterogeneous clients in an unsupervised manner while preserving the clients' privacy, which has emerged as a significant challenge due to the lack of label guidance and the Non-Independent and Identically Distributed (non-IID) nature of clients. In real scenarios such as personalized recommendation and cross-device user profiling, the global cluster may be fragmented and distributed among different clients, and the clusters may exist at different granularities or even nested. Although Hierarchical Clustering (HC) is considered promising for exploring such distributions, the sophisticated recursive clustering process makes it more computationally expensive and vulnerable to privacy exposure, thus relatively unexplored under the federated learning scenario. This paper introduces an efficient one-shot hierarchical FC framework that performs client-end distribution exploration and server-end distribution aggregation through one-way prototype-level communication from clients to the server. A fine partition mechanism is developed to generate successive clusterlets to describe the complex landscape of the clients' clusters. Then, a multi-granular learning mechanism on the server is proposed to fuse the clusterlets, even when they have inconsistent granularities generated from different clients. It turns out that the complex cluster distributions across clients can be efficiently explored, and extensive experiments comparing state-of-the-art methods on ten public datasets demonstrate the superiority of the proposed method.
Paper Structure (24 sections, 2 theorems, 24 equations, 8 figures, 4 tables, 2 algorithms)

This paper contains 24 sections, 2 theorems, 24 equations, 8 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Federated Clustering
  4. Hierarchical Clustering
  5. Proposed Method
  6. Problem Statement
  7. FCPL: Fine-grained Competitive Penalized Learning on the Clients
  8. MCPL: Multi-granular Competitive Penalized Learning on the Server
  9. Representation Aggregation-Enhanced Federated Clustering
  10. Overall Fed-HIRE Framework
  11. Experiment
  12. Experimental Settings
  13. Clustering Performance Evaluation
  14. Ablation Study
  15. Scalability Evaluation
  16. ...and 9 more sections

Key Result

Theorem A.1

The overall time complexity of the proposed Fed-HIRE is $O(M d k_0 N)$.

Figures (8)

  • Figure 1: Non-IID assumption distribution vs. non-IID with incomplete cluster distribution. (a) relies on a typical non-IID assumption that the complete distribution of clusters within each client can be sufficiently reflected. By contrast, (b) shows a more realistic scenario that clusters are fragmented into several clusterlets and distributed across different clients, which can easily lead to serious distortions at the server in aggregating global clusters.
  • Figure 2: Overview of Fed-HIRE. Each client performs FCPL to extract local clusterlets and uploads only their centroids to the server. The server then executes MCPL to derive multiple granularities $k$s and their corresponding partitions. Here, $k_1$ and $k_\Delta$ denote the finest and coarsest granularity levels, respectively. These multi-granular clusterlets form a structured hierarchy in a bottom-up manner. The resulting multi-granular distributions are embedded into enhanced representations. Given a target number of clusters $k^*$, the final clustering outcome is driven by feature-cluster weights learning.
  • Figure 3: Ablation study of granularity level. As more hierarchies are incorporated, the clustering performance improves.
  • Figure 4: Performance under varying number of clients.
  • Figure 5: Comparison of execution time with (a) increasing $N$, (b) increasing $d$.
  • ...and 3 more figures

Theorems & Definitions (7)

  • Remark 1
  • Remark 2
  • Remark 3
  • Theorem A.1
  • Proof A.1
  • Theorem A.2
  • Proof A.2