Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views

Xinyue Chen, Yazhou Ren, Jie Xu, Fangfei Lin, Xiaorong Pu, Yang Yang

TL;DR

A novel FedMVC framework is proposed, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap, and develops a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views.

Abstract

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Existing approaches often assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. Despite their success, these methods also present limitations when dealing with practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of both single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel FedMVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods. The code is available at \url{https://github.com/5Martina5/FMCSC}.

Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views

TL;DR

A novel FedMVC framework is proposed, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap, and develops a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views.

Abstract

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Existing approaches often assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. Despite their success, these methods also present limitations when dealing with practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of both single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel FedMVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods. The code is available at \url{https://github.com/5Martina5/FMCSC}.

Paper Structure

This paper contains 26 sections, 5 theorems, 38 equations, 9 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Problem Definition
  5. Cross-Client Consensus Pre-training
  6. Local-Synergistic Contrast
  7. Global-Specific Weighting Aggregation
  8. Experiments
  9. Experimental Settings
  10. Results and Analysis
  11. Attributes of Federated Learning
  12. Conclusion
  13. Framework of the Proposed Algorithm
  14. More Related Work
  15. Theoretical Analysis
  16. ...and 11 more sections

Key Result

Theorem 1

Assume $\delta_m, \delta_p\in(0,1)$ such that $p\left(\mathbf{h}_{i}^v \mid \mathbf{h}_{i} \right)>\delta_m$, $i=1,2, \cdots, \left|\mathcal{M}_{m}\right|$ and $p\left(\mathbf{h}_{i}^g \mid \mathbf{h}_{i} \right)=p\left(\mathbf{z}_{i}^v \mid \mathbf{h}_{i} \right)>\delta_p$, $i=1,2, \cdots, \left|\m

Figures (9)

  • Figure 1: The framework of FMCSC. Initially, each client conducts cross-client consensus pre-training to alleviate model misalignment (Section 3.2). Then, all clients begin training using the designed local-synergistic contrast (Section 3.3) and upload their local models to the server. The server performs global-specific weighting aggregation and distributes multiple heterogeneous global models to all clients (Section 3.4). Finally, leveraging global models received from the server, clients discover complementary cluster structures across all clients.
  • Figure 2: ACC vs. $\tau_m$ and $\tau_p$.
  • Figure 3: Visualization on model misalignment.
  • Figure 4: (a) Effect of samples per client on generalization performance. (b) Scalability with the number of clients on Multi-Fashion. (c) Sensitivity under privacy constraints when $M/S$ = 2:1.
  • Figure 5: Comparison strategies.
  • ...and 4 more figures

Theorems & Definitions (9)

  • Theorem 1
  • Theorem 2
  • proof
  • Lemma 1
  • proof
  • Lemma 2: Generalization Bounds for Domain Adaptation ben2010theoryben2006analysis
  • Lemma 3
  • proof
  • proof